Absence of an adipogenic effect of rosiglitazone on mature 3T3-L1 adipocytes: increase of lipid catabolism and reduction of adipokine expression

Aims/hypothesis The thiazolidinedione (TZD) rosiglitazone is a peroxisome proliferator-activated receptor-γ agonist that induces adipocyte differentiation and, hence, lipid accumulation. This is in apparent contrast to the long-term glucose-lowering, insulin-sensitising effect of rosiglitazone. We tested whether the action of rosiglitazone involves specific effects on mature adipocytes, which are different from those on preadipocytes.
Introduction
Rosiglitazone (BRL-49653), a thiazolidinedione (TZD) drug, is used in the treatment of type 2 diabetes mellitus [1]. It has been proposed that the glucose-lowering action of TZDs is mainly mediated by activating peroxisome proliferator-activated receptor (PPAR)-γ (PPARG) [2, 3]. PPARG is most abundantly produced in adipose tissue, suggesting that this is the primary site of action of TZDs [4]. Proposed mechanisms underlying the adipocyte-mediated glucose-lowering action of TZDs are the trapping of fatty acids inside adipocytes and away from muscle, the so-called ‘fatty acid steal’ hypothesis and the altering adipokine release [1].
PPARG is a critical transcription factor in adipogenesis [5]. Its production increases strongly during adipocyte differentiation [6]. By activating PPARG, rosiglitazone promotes adipocyte differentiation in vitro [7, 8]. An increase in the number of small adipocytes and fat mass by TZDs in both animal models and human subjects, suggests that adipocyte differentiation also occurs in vivo [9, 10]. Compared with preadipocytes, adipocytes have a much higher capacity for fatty acid uptake and lipid storage and are potentially more insulin-sensitive due to higher numbers of glucose transporters and insulin receptors [6]. By increasing the number of adipocytes, TZDs can lower circulating NEFA and improve lipid exposure of peripheral tissues, thus improving whole-body insulin sensitivity [1].
This hypothesis is clearly attractive, but other effects of TZDs may occur. It has been pointed out that the increased number of small adipocytes caused by TZD treatment is not only a result of the appearance of new adipocytes, but also due to the shrinkage and/or disappearance of existing mature adipocytes [10]. TZD-induced body weight gain in vivo is used as a marker of increased adiposity. However, there is evidence that this may also be due to fluid retention [11]. In fact, the fat mass may be unaffected or even decreased. In ob/ob mouse, whole body weight was increased, but the fat pad weight was not changed after rosiglitazone treatment [12]. In type 2 diabetes patients, the visceral fat area was found to be decreased [13]. ‘Fatty acid steal’ therefore is not just a matter of increased lipid storage.
The maturation of adipocytes is positively correlated with the production of adipokines. Several of these profoundly influence insulin sensitivity and glucose metabolism, e.g. leptin, adiponectin and resistin [14]. However, it has been observed that TZDs decrease the circulating level of individual adipokines such as leptin [15], resistin [16] and plasminogen activator inhibitor-1 [17], and also of cytokines [18]. A decreased production of these adipokines by TZDs seems to conflict with the adipogenic properties of these drugs.
The above discrepancy could be due to different effects on preadipocytes and mature adipocytes in adipose tissue. It is clear that TZDs stimulate preadipocytes into the process of differentiation. However, their effect on mature adipocytes is less clear. In vivo studies do not allow us to distinguish between the effects on these two cell types. Therefore, to study the effects of TZDs on mature adipocytes, we used in vitro differentiated mature 3T3-L1 adipocytes. In addition to the assessment of lipid accumulation and adipokine secretion, we used transcriptomics to obtain insight into the effects of rosiglitazone. Although high insulin and high glucose levels mimic the situation in vivo when rosiglitazone is administered, we also included low insulin and low glucose levels for comparison.
Materials and methods
Cell culture and sample preparation Murine 3T3-L1 fibroblasts (CL-173; American Type Culture Collection, Manassas, VA, US) were cultured and differentiated to adipocytes in vitro as described [19]. On day 8, differentiated cells, grown in DMEM/F-12 (1:1) supplemented with 10% fetal calf serum (Perbio Science, Erembodegem, Belgium), were treated with 0.5 μmol/l rosiglitazone maleate (GlaxoSmithKline, Worthing, UK) with low (4.5 mmol/l) glucose and (fetal calf serum level) insulin (Rosi), or with high (15.75 mmol/l) glucose and 1 μmol/l insulin (RosiIG). In parallel, control cells were cultured in the same medium with low (basal) or high glucose and insulin (IG). Each condition was created in quadruplicate. On day 10, two replications were collected separately for total RNA isolation using Trizol as described [19]. Cells of the other two replications were washed and incubated with the corresponding serum-free medium, supplemented with 2.2 mg/l transferrin and 10.6 nmol/l sodium selenite for 6 h. The number of cells was counted and culture medium proteins were collected as described [20]. The duplicated protein samples were pooled for later analysis. Three independent experiments were performed.
Oil red O staining An optimised Oil Red O staining method [21] was adapted for 3T3-L1 adipocytes cultured in a six-well plate. We added an additional quick wash with 70% ethanol after fixing and washing with water. After staining and washing, the neutral lipid-bound pigment was dissolved in DMSO and absorbance at 540 nm was measured. The staining for 3T3-L1 preadipocytes was used as background to correct for unspecific binding. The obtained absorbance values were further corrected for cell numbers measured in replicated wells. Five independent experiments were performed in duplicate, but in the first two experiments Basal and Rosi conditions were not measured.
Glycerol assay The culture media of cells that had been treated for 48 h were collected per condition, frozen in liquid N2, then stored at −80°C until analysis. The concentration of glycerol in the medium was measured by a quantitative enzymatic assay kit (Sigma, St Louis, MO, USA), according to the manufacturer’s instructions. The concentrations were corrected for cell numbers measured in replicated wells in parallel.
Microarray The mouse 10K_A oligo set (MWG, Ebersberg, Germany) and additional oligonucleotides of genes encoding secretory proteins and proteins involved in energy metabolism were printed in-house and further annotated as described [19]. Five total RNA samples of three independent experiments per condition were used for hybridisation against reference RNA, also as described [19].
Microarray data analysis The data were analysed as described [19]. Briefly, spots with an average intensity, over all arrays, of lower than twofold above average background were removed from further analysis, then the intensities of the remaining 4,019 spots were normalised against reference. Fold-change calculations, Student’s t tests and correlation test (Pearson) were performed in Excel (Microsoft). Fold-change equals ratio in the case of increase and equals −1/ratio in the case of decrease. Cut-off criteria for differential expression was set at fold-change >1.3 and p < 0.05. For genes with replicated spots, the average value of individual spots was used.Data were further analysed using Ingenuity Pathways Analysis (Ingenuity Systems, http://www.ingenuity.com). The program recognised 4,005 of 4,019 spot entries, and thus 3,718 genes. Differentially expressed genes that associated with a canonical pathway in the Ingenuity Pathways Knowledge Base were considered for evaluation. The significance of the association between the data set and the canonical pathway was measured in two ways: (1) a ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the canonical pathway is displayed; (2) Fischer’s exact test was used to calculate a p value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone. A pathway with a significance <0.05 was taken as significantly regulated.Quantitative real-time RT-PCR All six total RNA samples per treatment were used for quantitative real-time RT-PCR (Q-PCR) of 22 genes (primer information in Electronic supplementary material [ESM] Table 1). Q-PCR was performed as described [19]. Ribosomal protein S15 (Rps15) mRNA was used as reference.
One- and two-dimensional gel electrophoresis Protein samples were analysed by one-dimensional (1D) and two-dimensional (2D) gel electrophoresis, corrected for cell numbers, as described [19]. Brefeldin-A-treated adipocyte medium protein sample [20] was loaded on the same 1D gel, to verify protein secretion. Differentially expressed secreted protein bands were excised and identified by matrix-assisted laser desorption ionisation-time of flight mass spectrometry [22].
Western blotting The secretion level of adiponectin was analysed by 1D western blotting as described [20].
Results
Effect of rosiglitazone on the lipid content of mature adipocytes To assess the effect of rosiglitazone on lipid accumulation in mature adipocytes, we measured the lipid content of differentiated 3T3-L1 adipocytes. Visual inspection by microscopy showed no change in morphology and fat cell percentage in the population of 3T3-L1 adipocytes after 2 days of treatment with RosiIG or Rosi (0.5 μmol/l rosiglitazone at either high or low insulin/glucose levels, respectively), as compared with untreated adipocytes. Nevertheless, the lipid content, as detected by Oil Red O staining, was 10% lower (p < 0.001, n = 5) in RosiIG-treated cells, while no difference was observed in Rosi-treated cells (Fig. 1).
Fig. 1Oil Red O staining (ORO) for lipid content in 3T3-L1 adipocytes. Data are expressed as mean±SD. n = 5 for high glucose and insulin (control condition) (IG) and rosiglitazone with high insulin and glucose condition (RosiIG); n = 3 for basal and rosiglitazone with low insulin and glucose condition (Rosi). Blank bars: absorbance, relative value; filled bars: absorbance, relative value corrected for cell numbers. ** p < 0.01 compared with IG condition before cell number correction (t test), *** p < 0.001 compared with IG condition after cell number correction (t test)
Effect of rosiglitazone on gene expression To reveal the possible mechanisms for the decreased lipid content of rosiglitazone-treated 3T3-L1 adipocytes, we used DNA microarray to profile the gene expression. The complete analysis set was imported into Ingenuity Pathway Analysis for canonical pathway analysis. The significantly changed pathways were involved in metabolism, but not in signalling, such as PPAR signalling or insulin receptor signalling (Fig. 2). The expression of Pparg itself was downregulated 1.8- and 2.2-fold (p < 0.005) by Rosi and RosiIG treatment, respectively. Notably, pathways related to energy metabolism were among those that were most changed. Taking the high redundancy among these pathways in the Ingenuity database into account, we checked the individual pathways for redundancy. This showed that oxidative phosphorylation, fatty acid metabolism, glycolysis and gluconeogenesis, the pentose phosphate pathway, the citric acid (TCA) cycle, glycerolipid metabolism, glutathione metabolism, steroid (androgen, oestrogen and C21 steroids) metabolism and prostaglandin metabolism are independent pathways, while for the others it cannot be ruled out that they were identified as significant just because they contain genes overlapping with other pathways.
Fig. 2Regulated canonical pathways by rosiglitazone. Microarray data analysed by Ingenuity Pathway Analysis. Blank bars, with low insulin/glucose; filled bars, with high insulin/glucose. Bold line, thresholdFor these independently regulated pathways, we determined the direction of change by examining the individual genes in the pathways. The regulation of gene expression of rate-limiting or otherwise representative enzymes is presented in Table 1. High insulin/glucose attenuated the effect of rosiglitazone on some genes of complex I and II of the electron transport chain. However, high insulin/glucose treatment itself did not induce a change in the expression of these genes from the basal state. In general, rosiglitazone produced dominant effects, as indicated by the Pearson product moment correlation coefficient of 0.875 for the fold changes of the complete analysis set of Rosi/Basal and RosiIG/IG. The changes in the pathways indicate that rosiglitazone enhanced both energy metabolism at the TCA cycle and oxidative phosphorylation. The generated energy may be dispensed with the help of upregulated uncoupling protein 2 (Ucp2). Enhanced energy metabolism is mainly supported by upregulation of the pathways involved in uptake and activation of fatty acids and in beta-oxidation in both mitochondria and peroxisomes. In contrast, de novo fatty acid synthesis was downregulated, as indicated by fatty acid synthase (Fasn) and stearoyl-coenzyme A desaturase 1 (Scd1). The downregulation of triacylglycerol synthesis enzymes and upregulation of adipocyte lipase (also known as patatin-like phospholipase domain containing 2) together imply a decreased lipogenesis in mature adipocytes (Table 1).
Table 1Key enzymes of metabolism pathways regulated by rosiglitazonePathwayChangeGene symbolGene IDNameRosi/BasalRosiIG/IGFold-changep valueFold- changep valueTCA cycleUpIdh3a67834Isocitrate dehydrogenase 3 (NAD+) alpha2.530.0002.550.000Cs12974Citrate synthase1.950.0001.470.000Oxidative  phosphorylationUpNdufa568202NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 51.720.0091.200.235Sdhd66925Succinate dehydrogenase complex, subunit D1.830.0011.510.039Uqcrc122273Ubiquinol-cytochrome c reductase core protein I1.470.0071.330.017Cox7a112865Cytochrome c oxidase subunit VIIa polypeptide 11.780.0021.550.003Atp5h71679ATP synthase, H+ transporting, mitochondrial  F0 complex, subunit d1.710.0061.490.005Fatty acid uptake  and transportUpCd3612491CD36 antigen, fatty acid translocase2.540.0002.270.006Fabp411770Fatty acid bind protein 4, adipocyte1.640.0881.570.142Fatty acid  activationUpAcsl114081Acyl-CoA synthetase long-chain family member 12.500.0001.970.000Fatty acid beta  oxidationUpCrat12908Carnitine acetyltransferase1.450.0061.470.007Slc25a2057279Solute carrier family 25 (mitochondrial carnitine/acylcarnitine translocase), member 201.630.0031.220.092Acox111430Acyl-coenzyme A oxidase 1, palmitoyl2.700.0002.540.000Acaa1113868Acetyl-coenzyme A acyltransferase 1 (peroxisomal)1.670.0021.290.017Acaa252538Acetyl-coenzyme A acyltransferase 2 (mitochondrial)2.700.0001.940.000Fatty acid  synthesisDownFasn14104Fatty acid synthase−1.520.000−1.550.001Scd120249Stearoyl-CoA desaturase 1−3.650,000−4.310.000Triacylglycerol  synthesisDownPpap2b67916Phosphatidic acid phosphatase type 2B−1.380.001−1.480.000Dgat267800Diacylglycerol O-acyltransferase 2−1.310.003−1.370.006Lipid droplet  formationUp/downAdfp11520Adipose differentiation related protein5.970.0006.600.000Cav112389Caveolin, caveolae protein 1−2.200.000−2.300.001Glycerolipid lysisUpPnpla266853Patatin-like phospholipase domain  containing 21.520.0061.670.007Glycerol metabolismUpGk14933Glycerol kinase1.860.0071.880.003Gpd114555Glycerol-3-phosphate dehydrogenase 1 (soluble)2.070.0091.680.032Energy expenditureUpUcp222228Uncoupling protein 2 (mitochondrial,  proton carrier)1.780.0011.500.003Glutathione  metabolismUp/downMgst366447Microsomal glutathione S-transferase 32.110.0011.880.001Gsta414860Glutathione S-transferase A41.360.046−1.010.924Gstm114862Glutathione S-transferase M1−1.880.000−2.010.010Gstt114871Glutathione S-transferase theta 1−1.380.060−1.510.044Gstz114874Glutathione transferase zeta 1−1.440.001−1.570.015Prostaglandin  metabolismUp/downCbr3109857Carbonyl reductase 33.040.0003.960.000Ptges296979Prostaglandin E synthase 21.450.0121.610.000Ptgis19223Prostaglandin I2 (prostacyclin) synthase−1.710.002−1.410.004Steroids  metabolismUp/downHsd17b715490Hydroxysteroid 17-beta dehydrogenase 71.630.0031.730.002Nsdhl18194NAD(P) dependent steroid dehydrogenase-like1.510.0391.370.017Hsd11b115483Hydroxysteroid 11-beta dehydrogenase 1−2.730.001−2.050.004GlyconeogenesisUpFbp214120Fructose-1,6-bisphosphatase 22.920.0032.820.014Glycogen synthesisUpGyg27357Glycogenin1.520.0011.340.008Pyruvate  metabolismUpPdha118597Pyruvate dehydrogenase E1 alpha 11.380.0011.330.001Pdhb68263Pyruvate dehydrogenase (lipoamide) beta1.590.0051.610.004Pentose phosphateUpH6pd100198Hexose-6-phosphate dehydrogenase (glucose  1-dehydrogenase)1.600.0001.470.001Taldo121531Transaldolase 11.570.0011.330.036Leu catabolismDownIvd56357Isovaleryl coenzyme A dehydrogenase−1.420.004−1.720.000Mccc172039Methylcrotonoyl-coenzyme A carboxylase 1  (alpha)−1.260.026−1.350.031Mean value of microarray data (n = 5)Rosi/basal: the effect with low insulin/glucose; RosiIG/IG: the effect with high insulin/glucoseAccompanying fatty acid catabolism, glycerol activation by glycerol kinase and oxidation by glycerol phosphate dehydrogenase to dihydroxyacetone phosphate were upregulated, feeding glycolysis and gluconeogenesis. The enzymes catalysing glycolysis/gluconeogenesis were all upregulated, possibly in favour of gluconeogenesis. In addition, the upregulation of glycogenin, a gene priming glycogen synthesis, implies an increase of glycogen synthesis.Most genes in the amino acid metabolism pathways that were regulated also participate in fatty acid metabolism or in glycolysis. Therefore, we did not take amino acid metabolism pathways as independent rosiglitazone-regulated pathways. However, two key enzymes that catalyse the catabolism of leucine (Table 1) were downregulated. This implies that rosiglitazone saves amino acids from being used as fuel in energy metabolism.Glutathione metabolism was significantly changed by rosiglitazone treatment. Two glutathione S-transferases that detoxify lipid peroxides, microsomal glutathione S-transferase 3 and glutathione S-transferase A4, were upregulated, while other glutathione S-transferases were downregulated by rosiglitazone. This is in line with the upregulation of fatty acid oxidation by rosiglitazone.Microsomal glutathione S-transferase 3 is involved not only in glutathione metabolism, but also in eicosanoid metabolism, which is also regulated by rosiglitazone. The expression of genes encoding enzymes involved in prostaglandin metabolism implies that the conversion from prostaglandin H2 to E2, F2 and D2 may be enhanced, while conversion to I2 may be reduced.Cholesterol biosynthesis and steroid hormone metabolism were also affected by rosiglitazone. Expression of some steroid dehydrogenases were upregulated, while hydroxysteroid 11-beta dehydrogenase 1 was downregulated (Table 1). The latter has been reported as one of the beneficial effects of TZDs, leading to a decrease in the stress hormone cortisol [23].
Effect of rosiglitazone on the expression of genes encoding adipocyte-secreted proteins To assess whether protein secretion by mature adipocytes is altered by rosiglitazone, we checked genes encoding adipocyte-secreted proteins. The list was based on literature [24–27] and our previous work [20]. In total, 61 genes were present on the array. Il1b, Il6, Il10 and leptin showed very low signals (less than twofold of background) and were excluded from the analysis. Among the remaining 57 genes, only apolipoprotein E (Apoe) was upregulated by Rosi treatment, and more than 40% of the genes were significantly downregulated with both Rosi and RosiIG treatment (Table 2).
Table 2Effect of rosiglitazone on the expression of genes encoding adipocyte-secreted proteinsFunctionGene symbolGene IDNameRosi/BasalRosiIG/IGFold-changep valueFold-changep valueLipid  metabolismAdn11537Complement factor D (adipsin)−3.550.000−4.230.000C312266Complement component 3−2.250.000−2.680.001Lpl16956Lipoprotein lipase1.120.166−1.020.627Apoe11816Apolipoprotein E1.340.0261.180.145Insulin  sensitivityRetn57264Resistin−3.070.000−3.170.001Igf116000Insulin-like growth factor 1−1.550.012−1.580.001Pbef159027Pre-B-cell colony-enhancing factor 1−1.240.050−1.280.085Adipoq11450Adiponectin−1.090.462−1.250.108InflammationHp15439Haptoglobin−2.930.000−4.420.000Ptx319288Pentaxin-related gene−1.680.001−1.580.000Tgfb321809Transforming growth factor, beta 3−1.580.008−1.440.056B2m12010Beta-2 microglobulin−1.540.018−1.640.006Ccl220296Chemokine (C-C motif) ligand 2 (previously known as monocyte chemoattractant protein-1)−1.540.012−1.480.023Saa320210Serum amyloid A 3−1.490.047−1.380.001Cxcl1220315Chemokine (C-X-C motif) ligand 12−1.450.006−1.420.046Il6ra16194Interleukin 6 receptor, alpha−1.160.128−1.150.037Tgfb121803Transforming growth factor, beta 1−1.150.1631.020.914Tgfb221808Transforming growth factor, beta 2−1.150.306−1.070.584Il1816173Interleukin 18−1.150.224−1.010.937D17Wsu104e28106DNA segment, Chr 17, Wayne State University 104,  expressed (previously known as IL25)−1.030.794−1.160.088Lgals116852Lectin, galactose binding, soluble 11.050.7741.080.590Lcn216819Lipocalin 21.110.2281.200.179Mif17319Macrophage migration inhibitory factor1.180.0761.170.137Vascular  functionAdm11535Adrenomedullin−1.410.003−1.500.000Agt11606Angiotensinogen−1.220.161−1.150.150Serpinf120317Serine (or cysteine) peptidase inhibitor, clade F, member 1  (previously known as pigment epithelium derived factor)−1.150.237−1.250.187Vegfa22339Vascular endothelial growth factor A−1.030.724−1.080.644Apln30878Apelin1.010.9691.020.790Serpine118787Serine (or cysteine) peptidase inhibitor, clade E, member 1  (previously known as plasminogen activator inhibitor-1)−1.010.8971.270.013AntioxidantSod320657Superoxide dismutase 3, extracellular−1.090.500−1.390.036Mt117748Metallothionein 1−1.110.577−1.120.417Extracellular  matrix  componentNid218074Nidogen 2−1.640.002−1.960.003Sparc20692Secreted acidic cysteine rich glycoprotein−1.630.000−1.560.000Col5a212832Procollagen, type V, alpha 2−1.620.000−1.620.003Col5a112831Procollagen, type V, alpha 1−1.590.003−1.480.013Col1a212843Procollagen, type I, alpha 2−1.640.020−1.400.002Col1a112842Procollagen, type I, alpha 1−1.600.065−1.380.020Col3a112825Procollagen, type III, alpha 1−1.520.024−1.590.002Col4a512830Procollagen, type IV, alpha 5−1.440.034−1.370.019Col6a212834Procollagen, type VI, alpha 2−1.320.011−1.190.108Col6a112833Procollagen, type VI, alpha 1−1.310.1171.050.740Col5a353867Procollagen, type V, alpha 3−1.200.046−1.290.076Col4a112826Procollagen, type IV, alpha 1−1.160.114−1.160.069Col4a212827Procollagen, type IV, alpha 2−1.160.153−1.290.003Nid118073Nidogen 1−1.040.7231.040.767Col6a312835Procollagen, type VI, alpha 3−1.030.690−1.010.960Extracellular  matrix  processingMmp317392Matrix metalloproteinase 3−2.010.003−2.020.013Lox16948Lysyl oxidase−1.870.005−2.170.002Timp221858Tissue inhibitor of metalloproteinase 2−1.810.002−1.920.000Mmp217390Matrix metalloproteinase 2−1.440.054−1.300.100Timp121857Tissue inhibitor of metalloproteinase 1−1.320.095−1.300.055Pcolce18542Procollagen C-endopeptidase enhancer protein−1.310.029−1.480.002Mmp1958223Matrix metalloproteinase 19−1.010.848−1.080.347Mmp1117385Matrix metalloproteinase 111.050.860−1.120.413Timp4110595Tissue inhibitor of metalloproteinase 41.060.6721.250.055MiscellaneousGal14419Galanin−3.040.000−2.890.000Gsn227753Gelsolin−1.630.000−1.520.000Ppic19038Peptidylprolyl isomerase C−1.270.049−1.380.001Mean value of microarray data (n = 5)Rosi/basal: the effect with low insulin/glucose; RosiIG/IG: the effect with high insulin/glucoseInterestingly, the general downregulation of adipokines was also observed for other genes encoding secreted proteins. We used Gene Ontology to cluster the expressed genes on the microarray into secreted protein encoding genes and intracellular/unknown location protein-encoding genes (additional gene expression information is available at www.foodbioactives.nl). In the first cluster, more genes were significantly downregulated (Fig. 3a and c). Of 477 genes, 24.9 and 25.8% were significantly downregulated, while 9.6 and 6.7% were upregulated by Rosi and RosiIG, respectively. Of the upregulated genes, most encode multi-location proteins, which can also reside in the nucleus, mitochondria and/or membranes. If these genes are omitted from the analysis, only seven genes were upregulated (1.5%). In the cellular protein cluster, by contrast, the majority of the regulated genes were upregulated (Fig. 3b and d). Of 3,382 genes, 10.3 and 10.1% were significantly upregulated, while 5.1 and 5.0% were downregulated by Rosi and RosiIG treatment, respectively.
Fig. 3Regulation pattern by rosiglitazone of genes encoding extracellular proteins and of genes encoding intracellular proteins or proteins of unknown location. a, c: extracellular pattern; b, d: intracellular pattern. a, b: rosiglitazone with low insulin/glucose; c, d: rosiglitazone with high insulin/glucoseTo complement array data, we used 2D and 1D gel electrophoresis and western blotting to examine the effect of rosiglitazone on secretion. With regard to complement C3, a 30% reduction of secretion by Rosi or RosiIG treatment was observed on the 1D gel (Fig. 4). This is less than the change observed at the gene expression level. Nine spots on the 2D gel were identified as adipsin. We assessed the changes for individual spots, as well as for all spots in total. In both cases, the decrease effected by rosiglitazone at the gene expression level was similar to the decrease found at the secreted protein level with RosiIG treatment. However, this decrease at the protein level was less prominent with Rosi treatment (Fig. 5). The secretion of adiponectin was also analysed by western blotting. A slight increase with rosiglitazone was detected (Fig. 6), although there was no change at the gene expression level.
Fig. 4The effect of rosiglitazone on the secretion of complement C3 as measured by 1D gel analysis. The marked bands (arrows) were identified as C3. One representative image is shown. A sample treated by Brefeldin A (+BFA) was used to verify secretionFig. 5The effect of rosiglitazone on the secretion of adipsin as measured by 2D gel analysis. A section of one representative 2D gel image is shown for an individual adipsin spot (squares) from a low glucose and insulin control condition (Basal), b rosiglitazone with low insulin and glucose condition (Rosi), c high glucose and insulin control condition (IG) and d rosiglitazone with high insulin and glucose condition (RosiIG). e Total quantity of adipsin in the culture medium of 3T3-L1 adipocytes expressed in arbitrary units (AU)Fig. 6The effect of rosiglitazone on the secretion of adiponectin as measured by western blotting. Data are expressed as mean ± SD (n = 3) and one representative western blot image is shown. *p < 0.05 compared with Basal condition (t test); **p < 0.01 compared with IG condition (t test). Basal, low glucose and insulin control condition; Rosi, rosiglitazone with low insulin and glucose condition; IG, high glucose and insulin control condition; RosiIG, rosiglitazone with high insulin and glucose condition
General confirmation of the microarray data by quantitative real-time RT-PCR analysis To confirm our microarray data, we checked the expression of 22 genes that encode secreted proteins or are related to lipid metabolism, by Q-PCR, using Rps15 as reference. Rps15 was chosen because of its constant expression behaviour on the arrays. A gene was selected as target gene because either itself or its family member showed changed expression based on microarray data. The comparison showed a strong correlation between array data and Q-PCR data (ESM Fig. 1), with Pearson correlation coefficients of 0.987 and 0.986 for Rosi/basal and RosiIG/IG, respectively.
The effect of rosiglitazone on lipolysis We assessed the glycerol level in the culture medium as a measure of the lipolysis of adipocytes. RosiIG treatment significantly increased the release of glycerol from 3T3-L1 mature adipocytes, whereas glycerol release was significantly decreased by Rosi treatment (Fig. 7).
Fig. 7The effect of rosiglitazone on the culture medium glycerol levels. Data are expressed as mean±SD (n = 3). *p < 0.05 compared with IG condition (t test); **p < 0.01 compared with Basal condition (t test). Basal, low glucose and insulin control condition; Rosi, rosiglitazone with low insulin and glucose condition; IG, high glucose and insulin control condition; RosiIG, rosiglitazone with high insulin and glucose condition
Discussion
As a PPARG agonist, rosiglitazone stimulates adipogenesis, which is accompanied by an increasing lipid content of the differentiating cells. However, our results showed that rosiglitazone does not induce an increase of the lipid content, but decreases it in mature adipocytes at high insulin/glucose level. The pathway analysis based on transcriptome data points out that an altered energy metabolism may underlie this phenomenon.
Although the level of insulin/glucose has some influence, the effect of rosiglitazone on the transcriptome was clearly dominant. The most significantly regulated pathways by rosiglitazone in 3T3-L1 adipocytes were fatty acid oxidation, the TCA cycle and oxidative phosphorylation. Besides an upregulation of fatty acid catabolism pathways, our data indicate that rosiglitazone encourages adipocytes to utilise fatty acids for energy production and spares glucose and amino acids by enhancing glycogen synthesis and inhibiting amino acid catabolism pathways. This is in line with the upregulation of Ucp2 and the downregulation of Scd1 expression. The increase in the level of UCP2 is correlated with a shift in substrate utilisation in favour of lipids as the dominant metabolic fuel [28]. SCD1 is involved in fatty acid synthesis, but it has also been shown that SCD1 deficiency activates metabolic pathways that promote beta-oxidation and decrease lipogenesis [29].
Our conclusions regarding energy metabolism are consistent with recent studies. Mitochondrial changes have been shown in response to TZD treatment in 3T3-L1 adipocytes [30], human adipocytes in vitro [31] and mouse adipocytes in vivo [12]. These studies show that TZDs, including rosiglitazone, upregulate fatty acid oxidation by increasing mitochondrial protein and gene expression, but not in a trans-differentiation manner [12, 30]. This has been confirmed by direct measurement of labelled palmitic acid oxidation [12] or indirect calorimetric calculation [32]. We also found that among the genes involved in protein biosynthesis, more than 80% of those upregulated by rosiglitazone were genes encoding mitochondrial ribosomal proteins. This is consistent with an increase of mitochondrial biogenesis and remodelling [12]. We speculate that the maintenance of increased mitochondrial mass and function utilises (part of) the energy that the cell no longer needs for lipid synthesis under rosiglitazone treatment.
A decrease in lipogenesis pathways accompanies the increased fatty acid catabolism pathways caused by rosiglitazone in mature adipocytes, as shown by decreased expression of genes encoding triacylglycerol synthesis enzymes and the genes encoding lipase. The downregulation of Fasn by TZD has also been observed in brown adipocytes [33]. Caveolin-1 (CAV1) is an important protein for efficient lipid droplet formation [34]. The downregulation of its gene expression supports a decrease of lipid accumulation. The upregulation of adipocyte differentiation related protein gene (Adfp), which encodes a lipid droplet-associated protein, seems to conflict with the decreased lipogenesis observed by us (Table 1). However, in mature 3T3-L1 adipocytes, ADFP is spatially associated with lipid droplets subjected to lipolysis and is suggested to play a role in lipolysis [35]. Furthermore, the protein level of ADFP in mature adipocytes is not determined by the mRNA expression, but by the amount of micro-lipid droplets [35]. Therefore the increase in Adfp expression might also be interpreted as being in agreement with upregulated lipolysis.
The pentose phosphate pathway provides NADPH to be used in reductive biosynthesis, including fatty acid biosynthesis [36]. Rosiglitazone enhances the pentose phosphate pathway, but inhibits fatty acid biosynthesis, while stimulating other NADPH-dependent pathways, such as glutathione detoxification, cholesterol and steroid biosynthesis, and prostaglandin metabolism (carbonyl reductase 3). The excess of NADPH may be a driving force behind the upregulation of these pathways by rosiglitazone.
The present study indicates that in mature 3T3-L1 adipocytes rosiglitazone enhances fatty acid catabolism, increases lipolysis and reduces lipogenesis pathways. This may lead to a net decrease of lipid content of the cells. Indeed, we observed increased lipolysis expressed in terms of an increased glycerol level in the medium and a decreased lipid content of the cells under RosiIG treatment. This is in line with the increased lipolysis effected by rosiglitazone in a recent study on rat white adipose tissue [37]. We did not observe increased lipolysis under Rosi treatment, where in fact the glycerol level was decreased rather than increased. The difference implies that the insulin/glucose level alters the effect of rosiglitazone. It is likely that at low insulin/glucose level, the ‘starved’ adipocytes use glycerol as an energy source, rather than releasing it into the medium. However, it should be noted that low insulin/glucose is not a realistic situation in vivo when rosiglitazone is administered. The decreased lipid content is consistent with the physiological effects of TZDs in both animal models [10, 38] and in humans [9], where TZDs increase the number of small adipocytes. In adipose tissue treated with TZDs, mature adipocytes can become smaller by actively burning fat, in parallel to the emerging of small immature adipocytes due to preadipocyte differentiation. With rosiglitazone, mature adipocytes take fatty acids for burning rather than for storage. This still fits the ‘fatty acid steal’ hypothesis [1] and can explain part of the glucose-lowering effects of rosiglitazone.
Rosiglitazone affects the function of mature adipocytes not only with regard to fat metabolism, but also with regard to adipokine secretion. Despite the limitations of the present study, namely that a mid-size array was used and that the expression changes of several genes could not be detected due to sensitivity, our data at both the mRNA level and the protein level confirmed that rosiglitazone represses adipokine expression, except adiponectin and Apoe. Since the majority of adipokines are negatively correlated with insulin sensitivity [39], the general repression may correct the disturbance of insulin sensitivity caused by obesity. Furthermore, our transcriptome analysis suggests that rosiglitazone induces a general decrease in expression of genes encoding secreted proteins. This implies a general mechanism, in which energy redistribution might be involved.
We observed that repression at the secretion level was less significant than at the gene expression level. One explanation is that the effect on protein lags behind the effect on mRNA. Another reason might be the influence of insulin. We have recently studied the role of insulin in 3T3-L1 adipocyte secretion [19]. We observed that at a low insulin level, the secretion of proteins is already suppressed. Consequently, the inhibitory effect of rosiglitazone on secretion is less prominent in the low insulin condition.
In contrast to most adipokines, adiponectin is reversely correlated with obesity, and its circulating level is upregulated by TZDs [40]. A functional PPAR-responsive element (PPRE) in the adiponectin promoter could be the reason for the stimulation [41]. We observed an upregulation only at the secretion level, but not at gene expression level. This is in line with the conclusion from another study, that although plasma levels of adiponectin were upregulated by TZDs, there was no significant effect on adiponectin gene expression [16].
Rosiglitazone is a potent PPARG agonist. It seems logical to assume that all these effects are mediated through regulation of PPARG. Indeed, we observed that rosiglitazone induced a significant decrease of Pparg expression in mature 3T3-L1 adipocytes. This is in contrast to an overwhelming increase during adipogenesis, but it is in line with several reports [42–44]. With respect to PPARG downstream effects, we found here that two PPRE-bearing genes, Cav1 and Scd1, were downregulated, and two other genes, adiponectin and lipoprotein lipase, remained unchanged, while most PPRE-bearing genes [45], such as CD36 antigen, fatty acid binding protein 4, adipocyte and acyl-coenzyme A oxidase 1, palmitoyl, were upregulated by rosiglitazone (Tables 1 and 2). The effect of TZDs on Scd1 in mature adipocytes is opposite to that in preadipocytes [46], and the effect on Cav1 is in contrast to the reports in other cell types [47, 48]. It has been known that PPARG acts with different transcriptional partners, co-repressors and/or co-activators as a complex, to activate or repress the expression of target genes [49, 50]. Possibly, the PPARG complex is modulated by rosiglitazone in mature adipocytes in a manner that is different from that occurring in preadipocytes and other cell types.
In conclusion, we have shown that rosiglitazone, in contrast to the stimulation of adipogenesis in preadipocytes, reduces adiposity of mature adipocytes by increasing fatty acid catabolism pathways. In addition, rosiglitazone represses the expression and secretion of adipokines of mature adipocytes. Our findings suggest that the change of adiposity as seen in vivo [13] may reflect a shift in balance between the effects of TZDs on preadipocytes and those on mature adipocytes, while the changes in circulating adipokine levels primarily result from an effect on mature adipocytes. These aspects all contribute to the therapeutic effects of TZDs.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
Table 1
Q-PCR primer sequences (DOC 26 kb)
Fig. 1
Comparison between microarray and quantitative real time RT-PCR analysis. The mean of the fold-change is shown. n = 5 for array n = 6 for RT-PCR. The lines at 1.3 and −1.3 fold changes indicate thresholds of differentially expression (DOC 51 kb)

adipokine
thiazolidinediones
mature adipocytes
adipose tissue
secretion
microarray
energy metabolism
lipogenesis
lipolysis


Autonomous Reovirus Strain Classification Using Filament-Coupled Antibodies

We previously described a filament-based antibody recognition assay (FARA) that generates ELISA-like sandwich structures immobilized on a filament. FARA allows the coupling of antibodies to precise locations along a filament, on-line fluorescence detection of captured pathogen, and feedback-directed filament motion. These properties suggest that this approach might be useful as an automated means to rapidly classify unknown pathogens. In this report, we describe validation of the novel decision tree aspect of this technology using mammalian reovirus. Based on available antibodies, we developed a decision tree algorithm to detect virus with increasing specificity at each level of the tree. Using three strains of reovirus and a bacteriophage control, our system correctly classified the reovirus strains at a concentration of 2 × 1012 virions ml−1 and M13K07 phage at 3 × 1011 virions ml−1. Classification of reovirus strain type 3 Dearing (T3D) required three levels of testing: general reovirus classification in level 1, serotype 3 classification in level 2, and final T3D strain classification in level 3. Strain T3SA + also required three levels of testing before a final classification was returned in level 3. Classification of strain type 1 Lang (T1L) required two levels of testing. M13K07 phage detection required only one level of testing for classification. These results indicate that automated pathogen classification using FARA is feasible. Furthermore, the simplicity of the design could be exploited for development of more complex sub-classification networks with additional levels and branches.
Introduction
Identification of specific pathogens is essential for the selection of pathogen-specific treatments, minimizing the spread of infection, and monitoring for long-term complications. Currently, specific pathogen identification is achieved through available RT-PCR and antibody-based strategies. In the clinic, these tests are usually applied consecutively to evaluate for the presence of the most likely to least likely pathogen based on patient findings. An automated classification strategy that is less dependent on clinical knowledge but achieves rapid accurate identification of a single pathogen from among a group of possible pathogens is currently unavailable.
Our previously described filament-based antibody recognition assay (FARA) employs antibodies immobilized at known locations along a filament to detect specific pathogens.9,10 The filament is pulled through a series of small reaction chambers, and pathogens, if present, are captured by filament-bound antibodies. Detection of pathogen binding is achieved by using a fluorescently labeled second antibody specific for the pathogen.
FARA was first reported using immobilized anti-M13K07 antibody to detect M13K07 phage.10 This virus and antibody pair provided a well-characterized test system to demonstrate the feasibility of a filament-based, pathogen-detection platform. However, in this first generation approach, the filaments were removed from the system for fluorescence scanning. Subsequent improvements to FARA include an integrated fluorescence detector and a feedback algorithm to control filament position.9 The integrated detector enables adaptive pathogen detection in which regions of interest along the filament can be reincubated in the appropriate reaction chambers to increase filament fluorescence when the initial signal is low.
In this report, we describe a small-scale test of the use of a simple classification tree together with feedback-controlled FARA to identify four viruses. The feedback feature of online FARA is used to guide the selection of subsequent tests within the classification tree. Unnecessary tests are not performed, and each subsequent test becomes more specific for a single pathogen. These results establish FARA as a robust platform for classification of diverse pathogens.
Materials and Methods
Cells and Viruses
Murine L929 (L) cells were cultured in suspension in Joklik’s modified Eagle’s medium supplemented to contain 5% fetal bovine serum, 2 mM l-glutamine, 100 U mL−1 penicillin, 100 μg mL−1 streptomycin, and 0.25 g mL−1 amphotericin-B. Reovirus strains T1L and T3D are laboratory stocks. T3SA + is a monoreassortant virus isolated from L cells co-infected with T1L and T3C44MA.1 T3SA + contains the S1 gene segment from the type 3 parental strain and all other gene segments from T1L.1 Reovirus particles were purified as previously described.1,3,6 L cells were inoculated with second-passage L-cell lysate stocks of twice plaque-purified reovirus at a multiplicity of infection of 10 plaque-forming units per cell. Virus was purified from infected cells by freon extraction and CsCl-gradient centrifugation. Purified M13K07 virus was obtained from the Vanderbilt Molecular Recognition Core.
Antibodies
Mouse monoclonal antibodies 4F2, 5C6, 8H6, and 9BG5 specific for reovirus proteins2,12,13 (Table 1) were purified from mouse hybridoma supernatants using Protein A column chromatography. Anti-M13K07 monoclonal antibody was purchased from Amersham Biosciences (Piscataway, NJ).
Table 1.Antibody specificity.AntibodyProtein specificityReovirus strainT1LT3DT3SA+5C6T1 σ1+−−9BG5T3 σ1−++8H6T1 and T3 μ1+++4F2T3D σ3−+−
Antibodies 4F2 and 8H6 were used for fluorescence detection of virus (step 4, Table 2) and were labeled with Alexa Fluor 555 or Alexa Fluor 647 (AF555 or AF647, Molecular Probes, Eugene, OR), respectively, according to the manufacturer’s instructions (Molecular Probes). Labeled antibodies were purified using PD-10 size-exclusion chromatography (Amersham Biosciences). Antibody concentration and number of fluores per labeled antibody were determined by using absorbance measurements at 280 nm and the peak absorbance wavelength of each label. Aliquots of both labeled and unlabeled antibodies were stored at −20°C, and working solutions were kept at 4°C. Final antibody concentration was adjusted immediately prior to experiments.
Table 2.The five reaction chambers in FARA processingChamberDescriptionSolutionIncubation timeChamber ID / Volume (mm / μl)1Block/Wash FilamentPBS-Ta15 min2/2352Virus IncubationUnknown Virus40 min1/60 3WashPBS-T1 min2/2354Detecting Antibody IncubationFluorescently Labeled Detecting Antibody5 min1/60 5WashPBS-T1 min2/235aTween-20 (0.1%) was added to PBS as a blocking agent.
Filament Preparation
Capture antibodies were passively adsorbed to the filament surface in groups of three by placing the filament across the concave teeth of a PhastGel sample applicator (Amersham Biosciences) (Fig. 1). Three capture-antibody regions, corresponding to the three levels of testing, were prepared along each filament using three applicator combs glued end to end. Antibody solution pipetted onto the filament without the comb spread unimpeded along the filament. Surface tension within the teeth of the comb overcame this tendency and produced a small, distinct circumferential band of immobilized antibody. Antibody was spotted in a volume of 0.75 μL and allowed to passively adsorb to the filament for 45 min in a humidified box. Following incubation at 25°C, filaments were rinsed in phosphate buffered saline with 0.1% Tween 20 (PBS-T) and threaded through the reaction chambers for virus detection experiments. Preliminary experiments were performed to determine the optimal concentration of each capture antibody.
Figure 1.Schematic of a device for adsorption of capture antibodies to the filament. The filament is placed within the concave teeth of a PhastGel applicator to localize the spotted antibody to a small circumferential band around the filament.
Red fingernail polish (Poisonberry, Noxel Corporartion, Hunt Valley, MO) was used as a visible and fluorescent fiducial marker to identify the leading and trailing edges of capture-antibody regions during experiments and during laser scanning. The polish was applied by pipette between the teeth of the comb flanking the antibody region. A simple bar code system was used to identify each of the three capture-antibody regions. The first and second regions were preceded by a single fiducial marker, and the third region was preceded by two fiducial markers.
Micro-reaction Chambers
Glass microreaction chambers were fashioned from 0.25 inch stock tubing into 75 mm lengths. The ends were flared outward to facilitate movement of the filament through the chambers. Interior diameters of the chambers were 1 or 2 mm depending on the required reaction volume (Table 2). Chambers were carefully positioned in a straight line on the top of a horizontal aluminum stage using machined aluminum mounts.9 Fine adjustment of chamber position was achieved by using oblong mounting holes on each mount. PBS-T was used in all wash chambers and for virus and labeled antibody solutions. Reovirus was used at a concentration of 2 × 1012 virions mL−1, and phage M13K07 was used at a concentration of 3 × 1011 virions mL−1. Detecting antibodies were present in the detecting-antibody chamber at a concentration of approximately 40 μg mL−1 for each antibody in the three antibody mixture. Solutions were added to appropriate chambers at the initiation of experiments and used for all three levels of testing (Table 2). If testing proceeded to level three, fluid loss from the chambers was monitored and replenished if necessary.
Filament Control
Movement of the filament and, therefore, the antibody bands through the chambers, was achieved by using a rotary stage to wind or unwind the filament around a spindle. A small weight was attached to the opposite end of the filament to maintain a constant tension. Filament positioning to within several microns was achieved by using a rotary stage encoder from Yaskawa Instruments (Waukegan, IL) and a custom control algorithm written as a LabView Virtual Instrument (VI) (National Instruments, Austin, TX). Parameters including filament speed and residence times within chambers were controlled by using the LabView software interface.
Between chambers, the speed of the filament was 1 cm sec−1. Within each chamber, the capture-antibody region undergoing processing was oscillated 2.5 cm back and forth at a speed of 1 cm sec−1 to increase interactions between immobilized molecular structures attached to the filament and the molecular species in solution. Within the detector, filament speed was 4 cm sec−1.
Lasers and PMTs
Filament fluorescence was measured by passing the filament between two diode lasers. The two laser excitation sources were attached to either side of a detection chamber (Fig. 2). Laser 1 (638 nm, 25 mW diode laser; Coherent, Santa Clara, CA) was used to excite the antibody tag AF647. Laser 2 (532 nm, 20 mW diode-pumped, solid state laser; B&W Tek, Inc., Newark, DE) was used to excite the antibody tag AF555. The effective power of laser 1 was reduced to approximately 5 mW by using a polarizer and an excitation slit. Laser 2 was not attenuated. Two Hamamatsu R928 photomultiplier tubes (PMT) were attached to the top and bottom of the sample chamber and powered by 850 and 800 V signals for the AF647 and AF555 channels, respectively. Current from the PMT was converted to voltage by using transimpedance amplifiers that amplified the signal by a factor of 106 for AF647 and 105 for AF555. Voltage was sampled at a rate of 800 samples sec−1 by using a digital acquisition board from National Instruments (DaqPAD 6020E). Fluorescence signal was acquired as a function of filament position by using LabView. Following fluorescence scanning for most experiments, filament regions were cut and scanned again in a microarray flatbed scanner (GenePix 4000B, Axon Instruments, Union City, CA).
Figure 2.Schematic of lasers and photomultipliers (PMT) to detect online fluorescence of the filament. The filament is moved (arrow) through a rectangular sample chamber with a laser excitation source attached to either side. Photomultiplier tubes are attached to the top and bottom of the chamber.
Filters
Filter sets were placed in the light path between the sample chamber and the photomultipliers (Fig. 2). Long-pass filters with cutoffs at 685 nm (Chroma, Rockingham, VT) and 665 nm (Melles Griot, Rochester, NY) were combined to reduce reflected laser light from the AF647 laser. For the AF555 channel, two long-pass filters (570 nm cutoff, Melles Griot) were combined with a bandpass filter centered at 565 nm (30 nm bandwidth, Chroma) to reduce reflected light.
Classification Algorithm
The LabView program coordinated all filament movement, filament scanning, and feedback control. Elements of the feedback control are described by the nodes of the decision tree used to classify the viruses shown in Fig. 3. The fluorescent fiducial markers on either side of the capture-antibody region produced characteristically sharp emission peaks. Since the physical location of the immobilized antibody positions between the markers was known, experimental conclusions were based on the distance of the first detected peak from the initial fluorescent marker.
Figure 3.Decision-tree algorithm to classify virus strains. The filament control program enters the decision tree from the top at level 1 and, based on the type of virus found at each level, follows different branches of the decision tree. When reovirus is detected at level one, the filament is moved forward to the next antibody region for level 2 testing. If a serotype 3 reovirus strain is detected (T3D or T3SA+ ) at level 2, testing continues to level 3 to distinguish between these strains.
The filament-control program was designed to find each peak, calculate its location along the filament, and make a decision about additional tests. Spatially localized fluorescence from the filament was measured as a 0–14 V signal from the transimpedance amplifier. Voltage data from the scan were used as input data by a peak-detection function in LabView. The LabView peak detector fit a quadratic polynomial to sequential sets of points depending on a width parameter entered by the user. Data were then compared to a threshold parameter, also entered by the user, to identify peaks. A binary decision to stop or continue testing was based on the location and number of peaks found. Peak-detection threshold parameters were defined as 0.3 V (approximately three times background) with a width of 30 data points, which corresponded to a width slightly larger than the physical width of the comb tooth.
A common set of parameters was used in all experiments based on previous work with M13K07.10 A filament region containing three capture antibodies and two fiducial markers was incubated within each chamber for the times shown in Table 2. Each of the capture-antibody regions contained a PBS negative control in the first position. In level one the second position was a mixture of 9BG5 and 5C6 (0.25 mg mL−1, 0.2 mg mL−1). The third position was anti-M13K07 (0.5 mg mL−1). In levels two and three, the negative control position was followed by 9BG5 (mg mL−1) in the second position and 5C6 (0.2 mg mL−1) in the third position.
The logic encoded in the decision tree shown in Fig. 3 was followed. In level 1, if fluorescence was detected from the anti-M13K07 position of the first region of the filament, a classification of M13K07 virus was made, and no further testing was conducted. If fluorescence was detected in the 9BG5/5C6 region, a classification of reovirus was made and the filament was advanced to the appropriate region for level 2 testing, where a more specific test for reovirus was performed. In level 2 testing, fluorescence from the 5C6 region indicated a serotype 1 reovirus, which in this scheme corresponded to reovirus T1L. A classification of T1L represented an endpoint in the decision tree, and testing was discontinued. Fluorescence from the 9BG5 region indicated the presence of a serotype 3 reovirus, and the program advanced the filament for further subtyping in level 3. Level 3 testing used AF555 conjugated 4F2 antibody as the detecting antibody and a second detection channel with a green excitation laser. If fluorescence was found in the 9BG5 region using this channel, a classification of reovirus T3D was made and testing along that branch ended. If no fluorescence was detected from the second channel for that region, the program ended with a classification of reovirus T3SA+ .
If no peaks were found between the fiducial markers, a message was generated indicating that no virus was found. Similarly, if a peak was detected in the negative control region of the filament, a warning message was generated. Although theoretically possible, this situation did not arise during testing.
Results
FARA utilizes a polyester filament with circumferential bands of immobilized antibody that is passed through a series of five glass micro-reaction chambers that are similar to the five major steps of an ELISA (Table 2). In the first reaction chamber of the FARA approach employed in this study, capture antibody coupled to the filament was rehydrated. In this chamber the filament also was blocked to prevent non-specific binding of virus. In the second chamber, the immobilized capture antibody was incubated with virus solution. In this chamber, if virus in solution bound to antibody coupled to the filament, then the virus became coupled to the filament via this interaction. In the third chamber, non-specifically bound virus was removed by washing before incubation with a fluorescently labeled detecting antibody in the fourth chamber. A final wash in the fifth chamber removed non-specifically bound detecting antibody. The capture antibody region was then passed through an integrated fluorescence detector.
In these experiments, the test virus was classified with greater specificity at each level of a decision tree (Fig. 3). Representative fluorescence signals in volts obtained during testing for phage M13K07 and reoviruses T1L, T3D, and T3SA+ are shown in Fig. 4. Labels for the PBS control position and the antibodies in each capture antibody position are shown adjacent to the filament. The specificity of each test antibody for its corresponding virus was high, with little or no cross-reactivity. No signal was detected in the PBS negative-control position.
Figure 4.Fluorescence intensity in volts as a function of filament position for M13K07 phage (A) and reoviruses T1L (B), T3D (C), and T3SA+ (D). The immobilized capture antibody positions on the filament are labeled within each panel. Captured virus was detected by a fluorescently labeled second antibody (anti-M13K07 [red], 8H6 [red], or 4F2 [green]). Corresponding images of the scanned filaments are shown at the top of each graph. Each panel shows a representative experiment of three performed.
Detection of M13K07 was achieved by level 1 testing. Strong fluorescence was observed in the anti-M13K07 position but not in the 9BG5/5C6 or PBS positions (Fig. 4a). This finding indicated capture of the phage M13K07 by the anti-M13K07 capture antibody. For detection of M13K07, AF647 conjugated anti-M13K07 was used. Because a classification of M13K07 was made, the other two capture regions of the filament were not evaluated.
Detection of the reovirus strains employed in this study required analysis beyond level 1. Level 1 testing indicated the presence of a reovirus for all three strains with a peak in the second position corresponding to virus capture by the 9BG5/5C6 antibody mixture. In level 2 testing, strain T1L was detected in the 5C6 antibody position but not in the 9BG5 or PBS positions (Fig. 4b). In this case, the capture and detecting antibodies differed. Because a reovirus T1L classification was made, the third capture region of the filament was not evaluated.
For reovirus T3D, level 1 testing indicated the presence of a reovirus with a peak in the second position corresponding to virus capture by 9BG5/5C6 (data not shown). Level 2 testing showed a distinct peak in the 9BG5 antibody position (Fig. 4c, red curve). In this case, 8H6 antibody labeled with AF647 was the detecting antibody indicating a type 3 reovirus. Level 3 testing showed a distinct peak in the 9BG5 antibody position (Fig. 4c, green curve). In this case, 4F2 antibody labeled with AF555 was the detecting antibody. Neither curve showed cross reactivity of reovirus T3D with the immobilized 5C6 region or the negative-control PBS region.
For reovirus T3SA+ , level 1 testing indicated the presence of a reovirus with a peak in the second position corresponding to virus capture by 9BG5/5C6 (data not shown). Level 2 testing yielded a distinct peak in the 9BG5 antibody position (Fig. 4d, red curve), like the result obtained with T3D. However, level 3 testing with 4F2 did not yield a positive signal in the 9BG5 antibody position, indicating that the captured virus was not T3D. No detectable cross reactivity was found in the 5C6 antibody region or the negative-control PBS region. Thus, the virus was classified as T3SA+.
Discussion
In this study, we demonstrated the feasibility of a FARA pathogen-classification approach using a well-characterized virus system. Three reovirus strains and one phage were successfully classified by performing sequential antibody-binding assays directed by the decision tree. The structure of the decision tree was based on published characteristics of each virus strain and the known specificity of each antibody. Because the capture antibodies were arrayed at known locations along the filament, and the fluorescence of the detection antibodies was measured as a function of filament location, an increase in filament fluorescence at a particular location indicated antibody interaction with the virus. For each virus strain tested, the expected fluorescence peaks were observed, and the correct classification was made. Moreover, successful transit of all decision tree branches was demonstrated.
The biochemical components of FARA are similar to those used in standard ELISAs. Both assays create a dual-antibody sandwich that results in similar detection limits for M13K07 for FARA and ELISA.10 One of the virus-specific antibodies acts as the capture antibody, and the second acts as the detecting antibody. The main difference in the biochemistry of the two assays is that, in the current implementation of FARA, enzyme amplification is not utilized. Like ELISA, FARA utilizes antibodies adsorbed to the surface of a substrate to capture virus from solution. The capacity of bound antibody to retain its antigen-binding activity is essential to the success of both assays. Each of the immobilized test antibodies in our system retained functionality and bound its corresponding virus (Fig. 4). The absence of peaks in the negative-control positions and in positions occupied by antibodies not specific to the test virus demonstrates minimal cross reactivity in the FARA platform employed in this study.
We chose reovirus as a test system to show clinical relevance and to avoid potential safety concerns in the laboratory. Reovirus has been used as a model to study mechanisms of viral pathogenesis in mice.14 Although it is a human pathogen, it is rarely associated with human disease.11 Many reovirus field-isolate strains have been characterized,4,7,8 and a broad array of antibodies are available that recognize reovirus with varying specificity.12
We found that not every antibody tested was suitable for use as an immobilized capture antibody. Neither antibody 8H6 nor 4F2 bound virus when immobilized on the filament, even after virus incubation time was increased to greater than 100 min (data not shown). It is possible that these antibodies undergo conformational changes when passively adsorbed to a solid substrate, rendering them inactive. Alternatively, these antibodies may be incapable of antigen binding when immobilized as a consequence of steric hindrance. Antibodies 8H6 and 4F2 recognize the μ1 and σ3 proteins, respectively,12 which form the bulk of the viral outer capsid.5 It is possible that extension of the viral attachment protein σ1 may shield the μ1 and σ3 proteins from binding to the 8H6 and 4F2 antibodies on the filament surface. This conclusion also is consistent with our observation that 9BG5 and 5C6 were both excellent capture antibodies, since they recognize the σ1 protein, which extends farthest from the viral capsid. The failure of the 8H6 and 4F2 antibodies to function as effective capture antibodies complicated our virus detection scheme. Although 8H6 is not serotype-specific and should bind all reovirus strains, we could not use this antibody as the immobilized capture antibody for level 1 testing. Antibodies 5C6 and 9BG5 are specific for serotype 1 and serotype 3 reovirus strains, respectively;12 therefore, we immobilized a mixture of these antibodies for detection of all reovirus isolates in level 1. Although 4F2 recognizes most type 3 strains, T3SA+ is a reassortant virus that contains a σ3-encoding gene segment from T1L.1 As a result, 4F2 does not recognize T3SA+ and is specific only for T3D in our scheme. Since 4F2 could not be used as a capture antibody in level 3, we incorporated a fluorescently labeled 4F2 antibody as a detecting antibody in solution.
Although the nature of the biochemical interactions is similar in FARA and ELISA, the capture antibody employed in a standard ELISA is static, and solutions are changed in the well-plate in a fixed sequence. In FARA, the capture antibody is attached to a mobile substrate, and solutions are changed by positioning the filament in different solution chambers. As we have shown previously, this gives FARA a sensitivity limit similar to ELISA on the order of 107 virus particles.10 A major advantage of FARA is that it is dynamic and allows modification of processing in response to results.9 Virus incubation time with the capture antibody can be reduced to as low as one minute depending on the virus concentration and antibody affinity, reducing the overall assay duration. On the other hand, the incubation time can be increased in order to increase sensitivity.9 Moreover, because the filament is mobile, the capture antibody is brought to the virus solution, thus enabling use of capture antibodies with different specificities to interrogate a single aliquot of virus solution.
Attachment of the capture antibody to a mobile filament in FARA makes accurate positioning of the filament and correct identification of the capture antibody regions essential for virus strain classification. These parameters are particularly important in multilevel testing of the type used in our study. A simple bar code system based on fluorescent fiducial markers was used to identify the leading edge of each of the capture antibody regions and position the filament in the reaction chambers. The pattern of the fluorescent marks also was used to identify the level of testing. Since our simple test system involved three levels, and testing always began in level 1, only searches for patterns containing one or two fluorescent marks corresponding to levels two and three were required. However, this simple approach allows for the incorporation of more complex bar codes using additional spots or patterns of spots.
This study demonstrates the feasibility of an automated system for diagnosing specific virus strains. Although the scheme we report is a relatively simple implementation of this system, more complex designs are possible. For example, each antibody capture region could contain additional antibodies. In addition, there is essentially no limit to the overall length of the filament, which would allow the incorporation of many additional testing regions. The sensitivity of FARA could also be increased by the use of quantum-dot labeled detection antibodies as well as a more sensitive fluorescence detection system. The most important requirement is the availability of a wide range of antibodies to the specific pathogens of interest. Such a strategy may have applications for detecting specific pathogens from complex mixtures. This approach would have both clinical and environmental applications.

fluorescence detection
pathogen detection
virus detection
virus classification


Impaired glucagon-like peptide-1-induced insulin secretion in carriers of transcription factor 7-like 2 (TCF7L2) gene polymorphisms

Aims/hypothesis Polymorphisms in the transcription factor 7-like 2 (TCF7L2) gene are associated with type 2 diabetes and reduced insulin secretion. The transcription factor TCF7L2 is an essential factor for glucagon-like peptide-1 (GLP-1) secretion from intestinal L cells. We studied whether a defect in the enteroinsular axis contributes to impaired insulin secretion in carriers of TCF7L2 polymorphisms.
Introduction
Genome-wide scans for diabetes susceptibility genes have been performed in various populations. In early 2006, it was reported that variants in the transcription factor 7-like 2 (TCF7L2) gene were strongly associated with an increased risk of type 2 diabetes in an Icelandic, a Danish and a US population [1]. The estimated population attributable risk of type 2 diabetes of this variation lies between 10 and 25%, which is in the range of the peroxisome proliferator-activated receptor γ gene (PPARγ) Pro12Ala (25% [2]) and the potassium inwardly-rectifying channel, subfamily J, member 11 gene (KCNJ11) Glu23Lys (15% [3]) polymorphisms.
In the initial report of Grant et al. [1], five single nucleotide polymorphisms (SNPs) within introns 3 and 4 of the TCF7L2 gene (rs12255372, rs7903146, rs7901695, rs11196205, rs7895340) were identified to associate with an increased risk of type 2 diabetes. This finding initiated a series of reports in various populations confirming the effect of these polymorphisms within the TCF7L2 gene on the type 2 diabetes risk [4–12]. Recently, the T allele of rs7903146 was identified as the variant that most strongly determines the risk of type 2 diabetes [13]. In carriers of the risk alleles for SNPs within TCF7L2 several studies additionally showed an impaired insulin secretion estimated from an OGTT or IVGTT [6, 7, 9, 10]. These studies indicate that progressive loss of insulin secretion might be the essential component of the phenotype which predisposes carriers of the TCF7L2 variant to develop type 2 diabetes. The pathogenic mechanism of the impaired insulin secretion due to polymorphisms in the TCF7L2 gene is not yet clear. It could be the consequence of a reduction in beta cell mass or could reflect a dysfunction of pancreatic beta cells. Furthermore, a defect in incretin-induced stimulus secretion coupling could mediate a reduction of insulin secretion in carriers of the TCF7L2 polymorphisms. The latter hypothesis is supported by the molecular mode of action of the transcription factor TCF7L2. TCF7L2 is an essential component of the wingless-type MMTV integration site family, member 1 (WNT) signalling pathway, which is crucial for the regulation of the glucagon gene (GCG) expression and the secretion of its product GLP-1 by the intestinal endocrine L cells [14]. Therefore, an alteration in the WNT signalling pathway through polymorphisms in the TCF7L2 gene might result in an altered GLP-1 response, which in turn could lead to a lower postprandial insulin secretion.
To test this hypothesis we analysed the associations of the above-described TCF7L2 polymorphisms with basal GLP-1 secretion and glucose-induced GLP-1 response during an OGTT in non-diabetic participants. We further studied the influence of the TCF7L2 SNPs on insulin secretion kinetics to i.v. administered glucose during an IVGTT and a hyperglycaemic clamp. In addition, we particularly investigated the influence on GLP-1-induced insulin secretion using a combined hyperglycaemic clamp with additional GLP-1 infusion and arginine bolus [15].
Methods
Participants We studied 1,110 non-diabetic participants by an OGTT (Table 1). Secretion patterns of GLP-1 were obtained in a subgroup of 155 participants. The anthropometric characteristics of this subgroup are shown in Table 2. All 1,110 participants were also genotyped for the following five SNPs in the TCF7L2 gene: rs7903146, rs12255372, rs7901695, rs11196205 and rs7895340.
Table 1Anthropometric and metabolic data from all 1,110 participants who underwent an OGTT rs7903146p value (ANOVA)p value (CC vs XT)GenotypeC/CC/TT/Tn54647490NGT/IGT461/85386/8863/270.0070.03Sex (female/male)346/200315/15950/400.130.64Age (years)39 ± 140 ± 141 ± 10.390.67BMI (kg/m2)29.5 ± 0.428.9 ± 0.428.2 ± 1.00.510.24Plasma glucose (mmol/l) Fasting 5.1 ± 0.025.1 ± 0.025.2 ± 0.070.250.39 2 h 6.1 ± 0.076.2 ± 0.076.7 ± 0.20.060.17Plasma insulin (pmol/l) Fasting62.0 ± 2.153.4 ± 1.749.8 ± 3.10.0040.001 2 h442 ± 19356 ± 17372 ± 340.120.04Insulin sensitivityOGTT (arbitrary units)16.8 ± 0.518.2 ± 0.517.8 ± 1.20.02/0.02a0.005/0.006aInsulin secretionOGTT (pmol/mmol)319 ± 5301 ± 5292 ± 100.003/0.04b0.0009/0.02bInsulin/glucose ratio (pmol/mmol)143 ± 5127 ± 5124 ± 130.003/0.03b0.001/0.01bData are means ± SEMp values were obtained using χ2 test or ANOVAAnalysis for rs12255372 and rs7901695 also revealed a significant difference in insulin secretion (p = 0.003 and p = 0.05), whereas for rs7895340 and rs11196205 no significant differences in insulin secretion were detected (p = 0.53 and 0.45)ap values are derived from multivariate linear regression models: adjusted for age, sex and BMIbp values are derived from multivariate linear regression models: adjusted for age, sex, BMI and insulin sensitivityTable 2Anthropometric and metabolic data of the subgroup of 155 participants with additional measurements of GLP-1 rs7903146p value (ANOVA)p value (CC vs XT)GenotypeC/CC/TT/Tn736715NGT/IGT64/948/1910/50.030.009Sex (female/male)50/2342/258/70.500.33Age (years)46 ± 147 ± 147 ± 30.990.96BMI (kg/m2)29.8 ± 0.730.3 ± 0.727.1 ± 1.10.150.88Plasma glucose (mmol/l) Fasting5.2 ± 0.055.3 ± 0.075.6 ± 0.20.170.21 2 h6.5 ± 0.17.0 ± 0.27.2 ± 0.50.220.10Insulin sensitivityOGTT (arbitrary units) 12.8 ± 0.713.6 ± 1.015.7 ± 2.60.74/0.92a0.78/0.75aInsulin secretionOGTT (pmol/mmol)304 ± 12287 ± 13235 ± 190.01/0.02b0.02/0.01bGLP-1 (pmol/l) 0 min 16.1 ± 0.917.3 ± 1.317.3 ± 2.00.910.88 30 min34.1 ± 2.138.8 ± 4.038.1 ± 3.50.450.36 120 min28.9 ± 1.529.0 ± 1.728.9 ± 2.40.870.88 Fold increase 0–30 min2.5 ± 0.22.7 ± 0.32.4 ± 0.40.770.84Data are means ± SEMp values were obtained using χ2 test or ANOVAAnalysis for rs7901695 also revealed a significant difference in insulin secretion (p = 0.02), whereas for rs7895340 and rs11196205 no significant differences in insulin secretion were detected (p = 0.73 and 0.76)ap values were derived from multivariate linear regression models: adjusted for age, sex and BMIbp values were derived from multivariate linear regression models: adjusted for age, sex, BMI and insulin sensitivityA subset of 210 participants were studied by an IVGTT combined with a euglycaemic–hyperinsulinaemic clamp to determine insulin secretion capacity and insulin sensitivity in one test [16] (Table 3).
Table 3Anthropometric and metabolic data of 210 participants who underwent a combined IVGTT and hyperinsulinaemic–euglycaemic clamp rs7903146p value (CC vs XT)GenotypeC/CX/Tn97113NGT/IGT80/1781/320.06Sex (female/male)59/3864/490.58Age (years)44 ± 145 ± 10.29BMI (kg/m2)29.0 ± 0.528.8 ± 0.50.82Insulin sensitivityclamp (μmol kg−1 min−1 [pmol/l]−1)0.07 ± 0.010.07 ± 0.010.77Insulin secretion indexa IVGTT C-peptide secretion (pmol/l)7,904 ± 3067,679 ± 3230.40 IVGTT insulin secretion (pmol/l)2,059 ± 1311,924 ± 1290.25 OGTT insulin secretion (pmol/mmol)314 ± 8292 ± 80.04Data are means ± SEMp values were obtained using χ2 test or unpaired t test or multivariate linear regression modelsNone of the four other SNPs in TCF7L2 (rs12255372, rs7901695, rs11196205 and rs7895340) affected significantly insulin secretion in the IVGTT (all p > 0.2)aAdjusted for age, sex and BMI, and insulin sensitivityAdditionally, 233 participants were studied by a hyperglycaemic clamp [17]. The participants were selected from three independent studies in the Netherlands and Germany. The German and the Dutch cohort from Utrecht consisted of NGT and IGT participants, the Dutch cohort from Hoorn consisted of IGT participants only (Table 4). All participants in our studies underwent a standard OGTT to define their glucose tolerance status. Details of the study groups have been described previously [17–19]. In the German subgroup (n = 73), the hyperglycaemic clamp was continued with an additional GLP-1 and arginine administration [15, 20–22].
Table 4Anthropometric and metabolic data of 233 participants who underwent a hyperglycaemic clamp and of 73 participants who underwent a modified hyperglycaemic clamp with additional GLP-1 and arginine administration GermanyThe Netherlands (Utrecht)The Netherlands (Hoorn)rs7903146p valuers7903146p valuers7903146p valueGenotypeC/CX/TC/CX/TC/CX/TSex (female/male)15/2024/140.085/3122/11<0.0117/1130/330.27NGT/IGT30/527/110.1331/526/70.530/280/66–Age (years)38 ± 240 ± 20.6647 ± 147 ± 10.9456 ± 158 ± 10.43BMI (kg/m2)26.1 ± 0.925.1 ± 0.60.4125.6 ± 0.726.2 ± 0.70.4929.2 ± 0.827.9 ± 0.40.13Plasma glucose (mmol/l) Fasting5.1 ± 0.15.0 ± 0.10.724.6 ± 0.14.8 ± 0.10.186.6 ± 0.16.6 ± 0.10.75 2 h glucose5.9 ± 0.36.5 ± 0.30.215.7 ± 0.35.6 ± 0.30.749.5 ± 0.19.5 ± 0.10.84Insulin sensitivity (μmol kg−1 min−1 [pmol/l]−1)0.15 ± 0.020.15 ± 0.010.900.17 ± 0.020.18 ± 0.020.600.11 ± 0.010.14 ± 0.010.21Insulin secretion index (pmol/l)a First phase (sum 0–10 min)1,067 ± 158815 ± 1050.18963 ± 75891 ± 960.76699 ± 70679 ± 720.68 Second phase308 ± 47291 ± 410.80389 ± 42376 ± 470.71304 ± 45303 ± 380.18 First phase GLP-12,049 ± 2581,466 ± 1750.03n.a.n.a.n.a.n.a. Second phase GLP-14,567 ± 5682,881 ± 3410.006n.a.n.a.n.a.n.a. Arginine2,680 ± 2262,252 ± 1810.40n.a.n.a.n.a.n.a.Data are means±SEp values were obtained using χ2 test, unpaired t test or multivariate linear regression modelsCarriers of the risk allele for SNP rs12255372 showed reduced first phase (p = 0.02) and second phase (p = 0.005) GLP-1-induced insulin secretionCarriers of the risk allele for SNP rs7901695 showed reduced second phase (p = 0.02) GLP-1-induced insulin secretionn.a. Not availableaAdjusted for age, sex and BMI, and insulin sensitivityThe participants were not related to each other. Informed written consent for all studies was obtained from all participants, and the local ethics committee approved the protocols.
Genotyping Detection of the TCF7L2 polymorphisms described by Grant et al. [1] was done using the TaqMan assay (Applied Biosystems, Forster City, CA, USA) in the German population. The TaqMan genotyping reaction was amplified on a GeneAmp PCR system 7000, and fluorescence was detected on an ABI PRISM 7000 sequence detector (Applied Biosystems). As a quality standard, we randomly included six positive (two homozygous wild-type allele carriers, two heterozygous and two homozygous risk allele carriers) and two negative (all components excluding DNA) sequenced controls in each TaqMan reader plate. Because all controls were correctly identified, we assumed that the genotyping error rate of this method did not exceed 0.3% [23].In the Dutch population, only rs7903146 was determined using an ABI PRISM 7900HT sequence detector (Applied Biosystems).
OGTT At 08:00 hours, participants ingested a solution containing 75 g glucose. Venous blood samples were obtained at 0, 30, 60, 90 and 120 min for determination of plasma glucose, insulin and C-peptide concentrations and 0, 30 and 120 min for determination of GLP-1 concentrations. The participants did not take any medication known to affect glucose tolerance or insulin sensitivity. Tests were performed after an overnight fast of 12 h.
Combined IVGTT and hyperinsulinaemic–euglycaemic clamp After an overnight fast and after baseline samples had been obtained, 0.3 g/kg bodyweight of a 20% (vol./vol.) glucose solution was given at time 0. Blood samples for the measurement of plasma glucose, plasma insulin and C-peptide were obtained at 2, 4, 6, 8, 10, 20, 30, 40, 50 and 60 min. After 60 min, a priming dose of insulin was given followed by an infusion (40 mU/m2) of short-acting human insulin for 120 min. A variable infusion of 20% glucose was started to maintain the plasma glucose concentration at 5.5 mmol/l. Blood samples for the measurement of plasma glucose were obtained at 5 min intervals throughout the clamp.
Hyperglycaemic clamp Hyperglycaemic clamps were performed at 10 mmol/l in all participants. The Dutch NGT and IGT participants underwent a 3 h clamp. In the German NGT participants, the clamp lasted for 2 h followed by the GLP-1 and arginine stimulation (see below). After an overnight fast, the participants received an i.v. glucose bolus to acutely raise glucose levels to 10 mmol/l. Plasma glucose levels were measured at the appropriate intervals to maintain a constant plasma glucose during the clamp. Blood samples for insulin were drawn at 2.5 min intervals during the first 10 min of the clamp and at 10–20 min intervals during the remainder. Exact details of the clamping procedures in the different study groups have been described previously [15, 18, 19].
Combined hyperglycaemic clamp This hyperglycaemic clamp combined with GLP-1 and arginine administration was performed as previously described [15, 20–22]. After 120 min of hyperglycaemic clamp at 10 mmol/l, a bolus of GLP-1 (0.6 pmol/kg) was given (human GLP-1(7-36)amide; Poly Peptide, Wolfenbüttel, Germany) followed by a continuous GLP-1 infusion (1.5 pmol kg−1 min−1) during the next 80 min. At 180 min, a bolus of 5 g arginine hydrochloride (Pharmacia & Upjohn, Erlangen, Germany) was injected over 45 s while the GLP-1 infusion was continued. Blood for the measurement of glucose, insulin, proinsulin and C-peptide was obtained during the time-points shown in Fig. 1. This clamp allows measurement of different aspects of stimulus secretion coupling: first and second phases of glucose-induced insulin secretion, GLP-1-induced insulin secretion, and the response to additional arginine administration.
Fig. 1Associations between the genotypes of rs7903146 polymorphism in the TCF7L2 gene with insulin secretion during a hyperglycaemic clamp in 73 German participants. Open circles CC; closed circles CT and TT. AIR Acute insulin response. Arrow Administration of 5 g arginine. The p values show the differences for first and second phases of glucose-induced insulin secretion, first and second phases of GLP-1-induced insulin secretion and acute insulin secretory response to arginine (AIR) (for calculation see ‘Methods’; insulin secretion is adjusted for insulin sensitivity)
Analytical procedures Plasma glucose was determined as previously described [14, 17, 20, 21]. GLP-1 was determined during the OGTT at baseline, 30 and 120 min. GLP-1 immunoreactivity was determined using an RIA specific for the C-terminus of the peptide [24]. Samples were stored at −80°C. The assay measures the sum of the intact peptide plus the primary metabolite, GLP-1(9–36)amide using the polyclonal anti-GLP-1 antibody 89390 and synthetic GLP-1(7–36)amide as standard.Plasma insulin and C-peptide concentrations in the German cohort were measured by a microparticle enzyme immunoassay (Abbott, Wiesbaden, Germany) and an RIA (Byk-Santec, Dietzenbach, Germany). In the participants from the Netherlands, insulin was measured using an in-house competitive RIA employing a polyclonal anti-insulin antibody (Caris 46), 125I-labelled insulin (IM 166; Amersham, Roosdaal, the Netherlands) as a tracer and regular insulin (Humulin; Eli Lilly, Indianapolis, IN, USA) as a standard.
Calculations Insulin secretion in the OGTT was assessed by calculating the AUC for C-peptide divided by the AUC for glucose (AUCCP/AUCGlc). AUCs were determined by the trapezoidal method. Furthermore, insulin secretion was calculated as insulin:glucose ratio by dividing (insulin at 30 min–insulin at 0 min) by (glucose at 30 min–glucose at 0 min). Insulin sensitivity during the OGTT was estimated from glucose and insulin values as proposed by Matsuda and DeFronzo [25].Insulin secretion during the IVGTT was calculated as the sum of C-peptide levels during the first 10 min after glucose administration. Insulin sensitivity during the hyperinsulinaemic–euglycaemic clamp was calculated by dividing the average glucose infusion rate during the last 40 min of the clamp by the average plasma insulin concentration during the same time interval.Insulin secretion during the hyperglycaemic clamp was calculated using insulin levels determined during the clamp. The first phase of insulin secretion was defined as the sum of the insulin levels during the first 10 min of the clamp. The second phase of insulin secretion was defined as the mean of the insulin values during the last 40 min (80–120 min, NGT group, Germany) or the last 30 min (150–180 min, NGT and IGT groups, the Netherlands) of the clamp.In the combined hyperglycaemic clamp with GLP-1 and arginine administration, first-phase GLP-1-induced insulin secretion was defined as the mean of the 125 and 130 min insulin levels, second-phase GLP-induced insulin secretion (plateau) was defined as the mean of the 160–180 min insulin levels. The acute insulin response to arginine was calculated as the mean of 182.5 and 185 min minus 180 min insulin levels [21].The insulin sensitivity index was determined by relating the glucose infusion rate to the plasma insulin concentration during the last 40 min (NGT, Germany) or 30 min of the clamp.
Statistical analysis Data are given as means ± SEM. Non-normally distributed parameters were logarithmically transformed to approximate linearity for statistical comparison. Distribution was tested for normality using the Shapiro–Wilk W test. Differences in anthropometrics and metabolic characteristics between genotypes were tested using ANOVA for the three genotype groups. The secretion indices were compared using multivariate regression models. In these models the trait was the dependent variable whereas age, sex, insulin sensitivity and genotype were the independent variables. Due to the relatively low number of participants who were homozygous for the rare alleles in the hyperglycaemic clamp group, a dominant model was used. A p value of <0.05 was considered to be statistically significant. The statistical software package JMP (SAS Institute, Cary, NC, USA) was used.
Results
Genetic variants in the TCF7L2 gene The SNPs described in the paper of Grant et al. [1] had an allelic frequency of the minor allele of 30% (rs7903146), 28% (rs12255372), 31% (rs7901695), 46% (rs11196205) and 45% (rs7895340) in our population. These polymorphisms were all in Hardy–Weinberg equilibrium and in linkage disequilibrium (Electronic supplementary material Table 1).
OGTT: glucose tolerance, insulin secretion, insulin sensitivity and GLP-1 levels The percentage of participants with IGT was significantly higher in carriers of the risk allele for rs7903146 (p = 0.007), rs12255372 (p = 0.02) and rs7901695 (p = 0.02).Insulin secretion assessed as AUCCP/AUCGlc during the OGTT was significantly reduced in participants with the risk alleles for rs7903146 and rs12255372 in the additive and dominant model both unadjusted and adjusted for relevant covariates. Participants with the risk allele for rs7901695 had a significantly decreased insulin secretion in the additive model (p = 0.02) (Table 1).Insulin sensitivity, estimated by the index of Matsuda and DeFronzo [25], was significantly higher in participants carrying the risk alleles for rs7903146 and rs1255372 in TCF7L2 (Table 1).In a subgroup of 155 participants we measured GLP-1 concentrations at baseline, at 30 min and 120 min after oral ingestion of 75 g glucose. The anthropometric characteristics are shown in (Table 1). Neither basal GLP-1 plasma levels nor GLP-1 levels during the OGTT significantly differed between the genotypes in any of the above-described SNPs in the TCF7L2 gene (Table 2).
Combined IVGTT and hyperinsulinaemic–euglycaemic clamp: glucose-induced insulin secretion and insulin sensitivity C-peptide and insulin values during the IVGTT were not significantly different between the genotypes in any of the above-described SNPs in the TCF7L2 gene. Table 3 shows the results for rs7903146 in the additive and dominant model adjusted for relevant covariates. Including the glucose tolerance status as an additional co-variate in the model did not affect the results. Interestingly, a lower insulin secretion in this subgroup of participants could be demonstrated when insulin secretion was calculated from the OGTT as described above (Table 3). Insulin sensitivity measured with the clamp technique was not affected by any of the genotypes (all p > 0.4).
Hyperglycaemic clamp: glucose-, GLP-1- and arginine-induced insulin secretion and insulin sensitivity First- and second-phase insulin secretion during the hyperglycaemic clamp were not significantly different between carriers and non-carriers of the risk allele for rs7903146 (Table 4) in any of the three populations from the Netherlands and from Germany. The other four SNPs in TCF7L2 which were described above were determined in the German population only and were not associated with glucose-induced insulin secretion (data not shown) during the hyperglycaemic clamp.The first phase of GLP-1-induced insulin secretion was significantly reduced in carriers of the risk alleles for rs7903146 (p = 0.03) and rs12255372 (p = 0.02). In addition, we found significant differences in the second phase of GLP-1-induced insulin secretion for rs7903146 (p = 0.006), rs12255372 (p = 0.005) and rs7901695 (p = 0.02) with carriers of the risk alleles having a significantly lower second phase of GLP-1-induced insulin secretion. Table 4 and Fig. 1 show the results for SNP rs7903146.In contrast to GLP-1-induced insulin secretion, arginine-induced insulin secretion was not affected by any of the analysed SNPs in TCF7L2 (Table 4).
Discussion
The variants in TCF7L2 described by Grant et al. [1] that mediate an increased risk of type 2 diabetes have been found to be associated with reduced insulin secretion [6, 7, 9, 10]. We could confirm this reduced insulin secretion in our study in a group of 1,110 German non-diabetic participants using C-peptide levels during an OGTT. Unexpectedly, the i.v. application of glucose during an IVGTT did not affect insulin secretion in carriers of the risk alleles for SNPs in TCF7L2. The same results were obtained using i.v. glucose challenge during a hyperglycaemic clamp in subgroups of the German population and a population from the Netherlands. The observed different results between an orally and i.v. administered glucose challenge generate the hypothesis that incretin-induced insulin secretion is affected by the variants in the TCF7L2 gene. Possible mechanisms explaining these findings include impaired incretin production or incretin signalling.
To address this issue we first measured basal GLP-1 concentrations and GLP-1 response during an OGTT. None of the tested variants that have been shown in the literature to exert a reproducible effect on type 2 diabetes were associated with basal GLP-1 levels or with GLP-1 response during the OGTT.
Furthermore, we combined the hyperglycaemic clamp with additional GLP-1 infusion to measure GLP-1-induced insulin secretion. Both acute (so called first phase) and prolonged (so called second phase) GLP-1-induced insulin secretion during the hyperglycaemic clamp were significantly impaired in carriers of the risk alleles in TCF7L2. These data suggest that participants carrying the risk alleles have an impaired insulin secretion due to an impairment of the GLP-1 signalling chain.
The putative molecular mechanism for this finding is not clear. As the transcription factor TCF7L2 plays an essential role in the WNT signalling pathway [14], which on the one hand is required for a normal development of the pancreas and the pancreatic islets [26] and on the other hand is crucial for the regulation of GLP-1 expression and secretion in intestinal L cells [14]. In theory, polymorphisms in the TCF7L2 gene might therefore through alteration of the WNT signalling pathway directly affect beta cell growth, beta cell differentiation and beta cell function. Furthermore insulin secretion might also be affected indirectly via the enteroinsular axis either through an impaired overall GLP-1 secretion or through a defective or dysfunctional GLP-1-induced insulin secretion. An impaired or dysfunctional GLP-1 effect might result first in a reduced postprandial insulin secretion and second, might influence stimulation of beta cell growth and beta cell differentiation.
Our data show that basal and glucose-induced GLP-1 secretion during the OGTT were not affected by any of the analysed SNPs, whereas the first and second phases of GLP-1-induced insulin secretion were significantly reduced in carriers of the risk alleles in three of the five above-described SNPs in the TCF7L2 gene, among them rs7903146, which is most strongly associated in the literature with type 2 diabetes [1, 6, 9, 12, 13]. The finding that basal and glucose-stimulated GLP-1 secretion were not influenced by the TCF7L2 polymorphisms indicates that a reduced GLP-1 secretion is not likely to explain the impaired insulin secretion in carriers of the risk genotypes, although it can not fully exclude an effect of these SNPs on GLP-1 levels. First, by measuring total GLP-1 levels, we may have missed a subtle defect in GLP-1 secretion, which may have been detected by measuring the active form of GLP-1. Second, systemic GLP-1 levels may not adequately reflect the level of the active hormone acting in the gut wall on the autonomic nervous system [27]. Third, impaired TCF7L2 activity might tissue-specifically reduce the GLP-1 levels in the brain, which are believed as well to be important for insulin secretion [28].
While we have no evidence for reduced GLP-1 secretion we have strong evidence for an impaired GLP-1-induced insulin secretion in carriers of the above-described polymorphisms in TCF7L2. The data show a reduced efficiency of GLP-1 to stimulate insulin secretion in pancreatic beta cells. Alterations of the GLP-1 signalling pathway in beta cells might thereby play a role. Recently the TCF7L2 gene was also identified in human pancreas [14]. Therefore variations in this transcription factor could specifically impair the transcription of genes involved in the incretin signalling chain, resulting in resistance of the pancreatic beta cells to GLP-1.
In contrast to the observed reduction in the first and second phases of GLP-1-induced insulin secretion, the arginine-induced insulin secretion was not significantly affected by the analysed SNPs in the TCF7L2 gene. The arginine bolus in the combined hyperglycaemic clamp produces a maximal challenge for the secretory capacity of the beta cell and can be considered as a surrogate for beta cell mass [15, 20]. The SNPs do not affect this maximal insulin secretion, suggesting that the variants in TCF7L2 do not influence beta cell mass, at least in the prediabetic state. In addition, impaired beta cell function may also include the efficiency of the conversion from proinsulin to insulin [21]. However, there was no evidence for this abnormality related to the variants in the TCF7L2 gene during the hyperglycaemic clamp (data not shown).
We also found that insulin sensitivity calculated from the OGTT using an established index [25] was significantly increased in participants carrying the risk alleles for all analysed SNPs in the TCF7L2 gene. This was also described in the study of Florez et al. [9]. When we measured insulin sensitivity with the gold standard, the euglycaemic–hyperinsulinaemic clamp and the hyperglycaemic–hyperinsulinaemic clamp, we found no association of the risk alleles with insulin sensitivity. Taken together, our data suggest that the variants in the TCF7L2 gene do not have a strong effect on insulin sensitivity.
In summary, our data show that variations in the TCF7L2 gene are associated with impaired GLP-1-induced insulin secretion. This might be consistent with a state of relative incretin resistance. Increasing the incretin levels by pharmacological tools may thus be a logical therapy to overcome impaired insulin secretion in carriers of the TCF7L2 variants.
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM 1
Linkage disequilibrium statistics (D′, r2) among the five SNPs reported by Grant et al. [1] in the German population (n=1,110) (PDF 17 KB).