Metabolomic and Proteomic Analysis of a Clonal Insulin-Producing β-Cell Line (INS-1 832/13) Céline Fernandez,† Ulrika Fransson,‡ Elna Hallgard,‡ Peter Spégel,‡ Cecilia Holm,† Morten Krogh,§ Kristofer Wårell,| Peter James,| and Hindrik Mulder*,‡ Unit of Molecular Endocrinology and Unit of Molecular Metabolism, Department of Experimental Medical Science and Lund University Diabetes Center, and Departments of Theoretical Physics and Immunotechnology, Lund University, Sweden Received August 22, 2007
Metabolites generated from fuel metabolism in pancreatic β-cells control exocytosis of insulin, a process which fails in type 2 diabetes. To identify and quantify these metabolites, global and unbiased analysis of cellular metabolism is required. To this end, polar metabolites, extracted from the clonal 832/13 β-cell line cultured at 2.8 and 16.7 mM glucose for 48 h, were derivatized followed by identification and quantification, using gas chromatography (GC) and mass spectrometry (MS). After culture at 16.7 mM glucose for 48 h, 832/13 β-cells exhibited a phenotype reminiscent of glucotoxicity with decreased content and secretion of insulin. The metabolomic analysis revealed alterations in the levels of 7 metabolites derived from glycolysis, the TCA cycle and pentose phosphate shunt, and 4 amino acids. Principal component analysis of the metabolite data showed two clusters, corresponding to the cells cultured at 2.8 and 16.7 mM glucose, respectively. Concurrent changes in protein expression were analyzed by 2-D gel electrophoresis followed by LC-MS/MS. The identities of 86 spots corresponding to 75 unique proteins that were significantly different in 832/13 β-cells cultured at 16.7 mM glucose were established. Only 5 of these were found to be metabolic enzymes that could be involved in the metabolomic alterations observed. Anticipated changes in metabolite levels in cells exposed to increased glucose were observed, while changes in enzyme levels were much less profound. This suggests that substrate availability, allosteric regulation, and/or post-translational modifications are more important determinants of metabolite levels than enzyme expression at the protein level. Keywords: clonal cells • gas chromatography • electrophoresis • mass spectrometry
Introduction Metabolism serves a critical role in pancreatic β-cells. Not only does it supply the cell with energy and substrates for normal cellular functions and homeostasis, for example, biosynthesis, replication, and membrane potential, it also acts as an intricate signaling machinery. β-Cell metabolism determines the rate of secretion of the hormone insulin, the key regulator of whole body metabolism. This process is termed stimulussecretion coupling and implies that metabolites and/or metabolic fluxes translate a change in extracellular glucose to appropriate exocytosis of insulin-containing granules. Thus, glucose is transported into the β-cell in proportion to the extracellular concentration of the sugar, and is fully metabo* Corresponding author: Hindrik Mulder, MD, Ph.D, Department of Experimental Medical Science, Section for Diabetes, Metabolism, and Endocrinology, Unit of Molecular Metabolism, Lund University, Biomedical Center B11, SE-221 84, Lund, Sweden. Fax: 46-46-222 4022. E-mail:
[email protected]. † Unit of Molecular Endocrinology, Department of Experimental Medical Science and Lund University Diabetes Center. ‡ Unit of Molecular Metabolism, Department of Experimental Medical Science and Lund University Diabetes Center. § Department of Theoretical Physics, Lund University. | Department of Immunotechnology, Lund University.
400 Journal of Proteome Research 2008, 7, 400–411 Published on Web 12/07/2007
lized to CO2 and H2O. This metabolism of glucose, primarily by glycolysis and in the tricarboxylic acid (TCA) cycle, generates the metabolic coupling signals that mediate the stimulatory effect of glucose on exocytosis of insulin. Concurrently, like in many other cell types, the availability of glucose and its metabolites will dictate the rate of biosynthesis of amino acids, proteins, and other macromolecules. However, very little fatty acid and glycogen are produced, since the pancreatic β-cell virtually lacks the synthetic machinery for these molecules. This lack implies a role of fuel metabolism in the β-cell foremost for control of stimulus-secretion coupling. Released insulin subsequently promotes glucose uptake into peripheral tissues, inhibits production of glucose in the liver while stimulating glycogen synthesis, and abolishes fatty acid mobilization from the adipose tissue. All forms of diabetes, with the hallmark elevation of plasma glucose, are ultimately caused by a deficient release of insulin. A possible cause of β-cell failure is impaired stimulus-secretion coupling. While there is great consensus on the importance of metabolic stimulus-secretion coupling in the pancreatic β-cell,1 a growing realization is that the precise manner, and not necessarily the extent, of glucose metabolism controls insulin release.2 To this end, accumulating evidence supports a critical 10.1021/pr070547d CCC: $40.75
2008 American Chemical Society
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Omics Analysis of Clonal β-Cells 3
role of anaplerosis in β-cell stimulus-secretion coupling. Anaplerosis denotes a process where carbons enter the TCA cycle in ways other than that catalyzed by the pyruvate dehydrogenase complex (PDC). Thus, pyruvate may enter the TCA cycle via carboxylation by pyruvate carboxylase (PC). However, this net addition of carbons must be balanced by exit of a similar number of carbons in order to maintain equilibrium in the TCA cycle. The resultant cataplerosis permits metabolites to leave the TCA cycle and act as metabolic coupling factors, either directly or indirectly. In fact, in β-cells, about half of the glucose-derived carbons enter the TCA cycle upon carboxylation,4 and if this process is blocked, insulin secretion is abolished.5 A critical determinant of insulin secretion is a metabolically driven increase in the ATP/ADP ratio, which closes ATP-sensitive K+-channels (KATP) in the plasma membrane.6 The subsequent depolarization opens voltagegated Ca2+-channels, and there is a subsequent surge in intracellular Ca2+, which triggers exocytosis. An important role for the metabolic coupling factors seems to be to sustain insulin exocytosis, which is transient if only intracellular Ca2+is raised. In this way, metabolic processes drive both the triggering, or KATP-dependent, and the amplifying, or KATP-independent, pathways of insulin secretion.6 In view of the crucial role of metabolism in β-cell stimulussecretion coupling, improved analysis of metabolites is warranted. The intermediary metabolites generated in β-cells constitute its “metabolome”, a concept analogous to the term “proteome” used in proteomics. Up to this point, information on the levels of metabolites, and their alterations under different conditions, has mostly been derived from biochemical measurements in cellular extracts. Typically, an enzyme reaction coupled with production of a molecule determined spectrophotometrically is employed. Such measurements, however, will only allow determination of one species of molecule at a time. Recently, like in other functional genomics approaches, the unbiased simultaneous identification and quantification of multiple metabolites have become feasible. A high resolution chromatographic separation (such as gas chromatography (GC) or high-performance liquid chromatography (HPLC)), with subsequent sensitive detection using mass spectrometry (MS), is capable of yielding both qualitative and quantitative information on metabolites.7 Thus far, GC-MS has mainly been used for quantification of metabolites in plant cells.8 Applying this approach to pancreatic β-cells would be a significant advancement, paving the way for a more thorough understanding of β-cell metabolism. For instance, identification of metabolites that change their levels under different conditions (e.g., low versus high glucose) may reveal critical metabolic pathways that could play a regulatory role in β-cell stimulussecretion coupling. Here, we have adapted the protocol developed by Fiehn and colleagues8 for use in pancreatic β-cells. Moreover, we have correlated our findings with changes in the proteome extracted from 832/13 β-cells under the same conditions.
Experimental Procedures The chemicals used for two-dimensional gel electrophoresis were from GE Amersham Biosciences (Uppsala, Sweden). All other materials were from Sigma (St. Louis, MO), unless otherwise stated. Cell Culture. The clonal β-cell line 832/13 was derived from the rat INS-1 insulinoma cell line by a transfection-selection strategy.9 The cell line was cultured in RPMI-1640 containing
11.1 mM D-glucose and supplemented with 10% fetal bovine serum, 100 U/mL penicillin, 100 µg/mL streptomycin, 10 mM HEPES, 2 mM glutamine, 1 mM sodium pyruvate, and 50 µM β-mercaptoethanol, at 37 °C in a humidified atmosphere containing 95% air and 5% CO2. Insulin Secretion. INS-1 832/13 cells were seeded in 24-well dishes and cultured for 48 h in complete RPMI medium containing 2.8 or 16.7 mM glucose prior to the assay. When assayed, the cells were kept in HEPES balanced salt solution (HBSS: 114 mM NaCl, 4.7 mM KCl, 1.2 mM KH2PO4, 1.16 mM MgSO4, 20 mM HEPES, 2.5 mM CaCl2, 25.5 mM NaHCO3, 0.2% BSA, pH 7.2) supplemented with 2.8 mM glucose for 2 h at 37 °C. Insulin secretion was then measured by static incubation of the cells for 1 h in 1 mL of HBSS containing 2.8 or 16.7 mM glucose. Insulin was measured by radioimmunoassay (RIA) using the Coat-a-Count kit (DPC, Los Angeles, CA), which recognizes human insulin and cross reacts approximately 20% with rat insulin. Insulin Content Measurement. INS-1 832/13 cells were cultured in 24-well dishes for 48 h at 2.8 or 16.7 mM glucose. Cells were washed in PBS, and 100 µL of H2O was added per well. The cells were then scraped off and sonicated. Afterward, the cells were centrifuged, and the supernatant was diluted 1:10 in acidified ethanol. The samples were stored at -20 °C until assay with RIA. Extraction and Derivatization of Metabolites from Clonal β-Cells for GC-MS Analysis. The clonal 832/13 β-cells were cultured in 10 cm plates at 2.8 or 16.7 mM glucose for 48 h, using four plates for each condition. The cells were washed once with 10 mM ice-cold MES buffer (pH 6.6) and then once with sterile water. Cell numbers were determined in a Bürker chamber and used for normalization of the metabolite levels. Samples were quenched by addition of 1 mL of methanol kept on dry ice until use. Cells were then scraped off the plates, and 20 µL of ribitol (2 mg/mL in water) was added as an internal standard. The mixture was kept at 70 °C for 15 min, followed by centrifugation at 12 200g for 3 min at room temperature. The supernatant was transferred to a glass tube with a Teflon cap, and 1 mL of water and 0.5 mL of chloroform were added. The tube was vortexed and subsequently centrifuged at 2380g for 30 min. The methanol/water phase was dried in a SpeedVac concentrator overnight. Derivatization was then carried out in two steps: first, carbonyls were protected by methoximation using 50 µL of a 20 mg/mL solution of methoxyamine hydrochloride in pyridine at 30 °C for 90 min; second, acidic groups were silylated using 80 µL of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) for 30 min at 37 °C. Finally, the samples were shaken for 1.5 h at room temperature. The same procedure was also carried out for a selection of metabolites that was commercially available (Sigma) to create a reference mass spectrum/retention index library, which was useful for identification of metabolites when searches in the NIST library 2.0 were not unambiguous. GC-MS Analysis. One microliter of the derivatized samples was injected in splitless mode onto a Trace GC gas chromatograph (Thermo Finnigan, San José, CA) equipped with a 30 m Rtx-5MS column with 250 µm internal diameter and 0.25 µm film thickness (Restek, Bellefonte, PA), as previously described.10 Each sample was run in triplicate. Detection was performed with a Polaris Q mass spectrometer (Thermo Finnigan, San José, CA). Mass spectra were recorded from 40 to 600 m/z at 0.7 scans per second. The chromatograms and mass spectra were processed by the Xcalibur software (Thermo Journal of Proteome Research • Vol. 7, No. 01, 2008 401
research articles Scientific, Waltham, MA). Peak detection and mass spectrum deconvolution were performed automatically. Peaks were identified using the NIST library 2.0 and/or the reference mass spectrum/retention index library. Peak areas were calculated using a selected quantification mass for each metabolite. Differences in metabolite content between 832/13 β-cells cultured at 2.8 mM or 16.7 mM glucose were analyzed by a nonparametric Mann–Whitney U-test. Principal component analysis (PCA) was performed with Unscrambler 6.11a (Camo, Oslo, Norway). The data were log10-transformed, to remove stronger influence from highly abundant metabolites, and centered. Protein Sample Preparation for Two-Dimensional Gel Electrophoresis. The clonal β-cells were cultured as described for metabolomics. The cells were washed with ice-cold HEM buffer (20 mM HEPES, 300 mM mannitol, and 1 mM EDTA) three times. After the last wash, 250 µL of lysis buffer (8 M urea, 4% (w/v) CHAPS, 60 mM DTT, 0.1 mg/mL DNase, 0.025 mg/mL RNase, and protease inhibitor cocktail) was added to each plate, and the cells were scraped off while kept on ice. Material from four plates cultured at 2.8 or 16.7 mM glucose was pooled. The cells were homogenized by 7 passages through a syringe and needle. The samples were sonicated five times for 2 s separated by 10 s waiting periods on ice. The homogenates were shaken at room temperature for 1 h and then centrifuged at 40 000g for 1 h. The protein concentration in each supernatant was determined using a 2D-Quant kit (GE Amersham Biosciences). Two-Dimensional Gel Electrophoresis. The samples were mixed with rehydration buffer (7 M urea, 2 M thiourea, 4% (w/v) CHAPS, 15 mM DTT, and 1.0% (w/v) IPG buffer, pH 4–7). Two hundred micrograms of protein and 450 µg of protein were loaded onto the analytical gels and preparative gels, respectively. Isoelectric focusing was performed on 24 cm immobilized pH gradient strips (pH 4–7). Each analytical sample was run in triplicate, while preparative gels were made in singlet. Active in-gel sample rehydration was performed at 30 V for 10 h for the analytical gels and for 12 h for the preparative gels using an Ettan IPGphor (GE Amersham Biosciences). The isoelectric focusing was carried out with a 30 min gradient from 0 to 500 V, a 2 h gradient to reach 4000 V, a 1 h gradient to reach 8000 V, and hold at 8000 V for 6.5 h for the analytical gels, or for 7 h for the preparative gels. The proteins were then reduced with dithiotreitol (1% w/v) and alkylated with iodoacetamide (4% w/v), as previously described.11 The second dimension was run in an Ettan DALT II system (GE Amersham Biosciences) on 12.5% T polyacrylamide gels at 10 W/gel. Gels were stained in ruthenium bathophenanthroline disulfonate (RuBPS) with an improved staining and destaining method.12 Protein Visualization, Image Analysis, and Statistics. The stained gels were scanned using a Typhoon scanner (GE Amersham Biosciences) at an excitation wavelength of 488 nm, an emission wavelength of 610 nm, and a resolution of 100 µm. The images were analyzed with ImageMaster 2D platinum software (GE Amersham Biosciences). Spots were detected and matched automatically and then inspected manually. Percentage spot volume, which is a normalized volume such that the sum of percentage volumes over all spots on a gel is 100%, was used to quantify protein abundances. The foldchange between the β-cells cultured at 2.8 and 16.7 mM glucose was calculated as the ratio between the mean values of the percent volumes of the spots present in the two groups. P-values were calculated using a two-tailed Mann–Whitney test, with missing values replaced by zero. The smallest possible 402
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Fernandez et al. P-value was 0.2, which was attained exactly when all percent volumes in one group were above all percent volumes in the other group and missing values, if present, only occurred in the group with low percent volumes. Only those proteins with a P-value of 0.2 were considered significant in this study. The number of significant proteins was 305 out of a total of 800, whereas 160 would have been expected by chance, corresponding to a false discovery rate of 0.52. The binomial P-value to obtain at least 305 proteins out of 800 with a probability of 0.2 is 3 ×10-32, so it confirmed that the gel experiment detected differences between the groups, even though the small number of gels makes the P-values and false discovery rate quite high. Statistics were performed with the statistical package R.13 Picking of Spots and LC-MS/MS Analysis. Gel spots of 2.0 mm in diameter were excised and digested with trypsin (Promega, Falkenberg, Sweden), using the Ettan Spot Handling Workstation (GE Amersham Biosciences). LC-MS/MS was performed on a Micromass CapLC unit (Waters, Stockolm, Sweden) with a Micromass Qtof Ultima (Waters, Sollentuna, Sweden), as previously described.14 Database Searching. The MS/MS data were analyzed using MASCOT (version 2.1.02; http://www.matrixscience.com) and Tandem (http://www.thegpm.org) against the IPI rat database version 3.29 (http://www.ebi.ac.uk/IPI/IPIhelp.htm), with random entries as previously described.15 Enzyme specificity was set to trypsin with up to one missed cleavage allowed. Cysteines were set as modified by iodoacetamide and variable methionine oxidation was allowed. The tolerance of the precursor ion was set to 0.1 Da for both parent and fragment ion matches. A MASCOT score over 62, corresponding to a P-value of 0.05, was required for a positive hit. The Proteios software (version 1.1) was used to generate a combined protein hits report from the two search engines with a false discovery rate cut off of 0.01.15
Results Insulin Secretion. Chronically elevated plasma glucose levels are thought to impair β-cell function.16 This process, termed glucotoxicity, has been proposed to be a pathogenetic factor in type 2 diabetes. We decided to create an in vitro model for glucotoxicity, by culturing the clonal 832/13 β-cell line at the supraphysiological glucose concentration 16.7 mM for 48 h. To examine whether the elevated glucose level had impacted β-cell function, we performed an insulin secretion assay; the results are shown in Figure 1. After preculture for 48 h at 2.8 mM glucose, insulin secretion increased 7.6-fold upon acute (1 h) stimulation with 16.7 mM glucose. In contrast, insulin secretion rose only 4.3-fold when the cells were precultured at 16.7 mM glucose. Under conditions where the KATP-channels were bypassed due to the addition of 35 mM KCl, to depolarize the plasma membrane, and 250 µM diazoxide, to maintain the KATP-channels in an open state, insulin secretion at 2.8 mM glucose was elevated by 6.6-fold in cells precultured at 2.8 mM glucose alone; a further 3-fold increase in insulin secretion occurred when glucose was increased to 16.7 mM. In contrast, under KATP-independent conditions, insulin secretion in cells precultured at 16.7 mM glucose rose only 1.9-fold upon stimulation with 16.7 mM glucose. Moreover, preculture at the high glucose concentration decreased insulin content by ∼80% (105 ( 50 versus 594 ( 144 ng insulin/mg protein; P < 0.05). Thus, we have created an in vitro model, which displays some aspects of glucotoxicity. Here, distinct changes in the metabolome and proteome could be anticipated, enabling us to develop a metabolomic and proteomic analysis of β-cells.
Omics Analysis of Clonal β-Cells
research articles Thus, ornithine, serine, and glutamine are negatively correlated to malate, fumarate, alanine, citrate, ribose-5-phosphate, and glucose. The first group is indicative of 832/13 β-cells cultured at 2.8 mM glucose and the second group of cells cultured at 16.7 mM glucose. To facilitate the understanding of the metabolic changes, the altered metabolites in their respective pathways have been illustrated in a cartoon (Figure 5).
Figure 1. Insulin secretion after culture in 2.8 or 16.7 mM glucose for 48 h. Insulin secretion by 832/13 β-cells was assayed following acute (1 h) exposure to 2.8 mM glucose (2.8) and 16.7 mM glucose (16.7), and under KATP-independent conditions (with 2.8 mM glucose or 16.7 mM glucose) in combination with 35 mM KCl plus 250 µM diazoxide. Black bars, prior culture at 2.8 mM glucose; white bars, prior culture at 16.7 mM glucose; K+ indicated experiment under KATP-independent conditions. Values are means ( SEM of 4 independent experiments.*P > 0.05, ***P > 0.001.
Metabolomics. To resolve a specific part (i.e., polar) of the metabolome of 832/13 β-cells cultured at 2.8 or 16.7 mM glucose for 48 h, nonpolar substances were removed by extraction with CHCl3 prior to derivatization and GC-MS analysis of the polar extract. Sixteen metabolites deriving from glycolysis and the TCA cycle and amino acids were identified, using the NIST library and/or our reference mass spectrum/ retention index library; representative chromatograms of extracted metabolites from 832/13 β-cells cultured at 2.8 and 16.7 mM glucose are shown in Figure 2. Out of the 16 metabolites, two were only detectable in the extracts of 832/13 cells cultured at 16.7 mM glucose: pyruvate and glucose6-phosphate. Eleven metabolites displayed significantly different levels at high versus low glucose (Figure 3). The TCA-cycle intermediates, citrate, malate, and fumarate, were all increased at high glucose; ribose-5-phosphate from the pentose phosphate shunt was also increased. Nine amino acids were detected, and their levels varied substantially. The levels of aspartic acid, glycine, threonine, and valine were not changed. Alanine and hydroxyproline were increased at high glucose, whereas glutamine and serine were decreased. Peak areas of the 16 identified metabolites deriving from 8 dishes of 832/13 β-cells cultured at 2.8 or 16.7 mM glucose, respectively, and run in triplicate were subjected to PCA. The samples were designated as described in Figure 4. In the first PCA, two samples, H2b and H3c, were situated far from the other samples and had high residual variance in combination with a high leverage; they were therefore considered as outliers and subsequently removed from the data set. Three principal components were found to be optimal. These explained 93% of the variation in the samples. The score plot (Figure 4A) shows two clusters, corresponding to the cells cultured at low and high glucose, respectively. It is evident from Figure 4A that the clustering is mainly formed along the first PC. The loading plot (Figure 4B) illustrates which metabolites mainly account for this clustering.
The variation in metabolite levels due to technical variability of the combined sample analysis by GC-MS, sample detection and sample stability after chemical modification was as a rule below 25% of the mean (with the exception of valine measured at 2.8 mM glucose; Tables 1 and 2). The biological variation was also below 25% of the mean for 75% and 85% of the metabolites identified from 832/13 β-cells cultured at 16.7 and 2.8 mM glucose, respectively (Tables 1 and 2). Glucose-6phosphate displayed the highest sample variability with a coefficient of variation close to 60% of the mean. Technical variation was lower than biological sample variability for 75% and 70% of the compounds identified in 832/13 β-cells cultured at 16.7 and 2.8 mM glucose, respectively (Tables 1 and 2). At both glucose conditions analyzed, determination of alanine, glycine, and valine contents was more affected by experimental error than by biological variability. Proteomics. Next, to examine whether the changes of metabolite levels could be attributed to changes in levels of proteins involved in cellular metabolism, a two-dimensional gel electrophoresis analysis of the 832/13 β-cells was performed; representative gels are shown in Figure 6. An average of 800 spots was detected on the analytical gels. Out of those, 305 spots were found to be significantly different on the gels derived from 832/13 β-cells cultured at 2.8 mM glucose versus those cultured at 16.7 mM glucose (P ) 0.2). Moreover, 187 of the differentially expressed proteins were localized on the preparative gels and submitted to analysis by LC-MS/MS. Out of the spots submitted to MS, 66 contained more than one protein and 86 a single protein. We then only further considered spots of which a single protein identification was made, corresponding to 75 unique proteins. Out of the proteins which were expressed significantly different at 16.7 mM versus 2.8 mM glucose, only 11 proteins were involved in metabolism. Glyceraldehyde-3-phosphate dehydrogenase, a key glycolytic enzyme, was downregulated at 16.7 mM glucose, in contrast to a previous report.17 Furthermore, we observed an upregulation of another glycolytic protein, R-enolase, as well as transaldolase, an enzyme in the pentose phosphate pathway. Glycerol-3-phosphate dehydrogenase, a key enzyme in the glycerol-phosphate shuttle was upregulated at 16.7 mM glucose. Among the downregulated proteins were isovaleryl-CoA dehydrogenase, phosphoglycerate mutase 1, and 6-phosphogluconolactonase. A list of all identified proteins is found in Tables 3 and 4. Two different spots identified as the β-subunit of ATP synthase were either found to be upregulated 2-fold in cells cultured at 16.7 mM or downregulated by ∼40% at 2.8 mM glucose. The estimated molecular weight of the downregulated protein was lower than the reported weight of the β-subunit of ATP synthase (56 kDa), while the upregulated spot corresponded better to the anticipated molecular weight. Thus, this apparent paradox can probably be explained by a molecular modification of the synthase, presumably cleavage. Journal of Proteome Research • Vol. 7, No. 01, 2008 403
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Figure 2. Representative GC-MS total ion chromatograms of 832/13 β-cells cultured in 2.8 (A) or 16.7 (B) mM glucose for 48 h. Peak identification and retention time in minutes: 1, pyruvate - 9.70; 2, alanine - 11.15; 3, glycine - 16.92; 4, serine - 18.51; 5, threonine 19.22; 6, malate - 21.81; 7, glutamine - 24.90; 8, ornithine - 29.13; 9, citrate - 29.36; 10, glucose - 31.16.
Figure 3. Metabolic profiling of 832/13 β-cells cultured in 2.8 or 16.7 mM glucose for 48 h. Metabolite content in 832/13 β-cells that were cultured at 2.8 mM glucose (black bars) and 16.7 mM glucose (white bars) for 48 h. Data were normalized against cell number and are expressed as mean ( SEM. Differences between the two conditions were analyzed using a nonparametric Mann–Whitney U-test. Three to four plates for each condition were analyzed in triplicate in the GC-MS. Abbreviations: Ala, alanine; Asp, aspartate; Glc6P, glucose 6-phosphate; Glc, glucose; Hydpro, hydroxyproline; Rib5P, ribose-5-phosphate; Thr, threonine.
Discussion To obtain information about metabolic regulation in β-cells, we have analyzed the metabolome in a highly differentiated clonal β-cell line, 832/13 cells,9 which was derived from the widely used rat INS-1 insulinoma cell line.18 The use of clonal β-cells for mechanistic studies of β-cell function is now widespread. The advantage of such a model system is that in contrast to islets isolated from animals or humans, which contain four major cell types; cell cultures are by definition homogeneous. However, the homogeneity of clonal β-cells has been questioned lately.9 Nevertheless, clonal cells can be expanded to quantities sufficient for 404
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diverse experimentation, a prerequisite for the present studies, while islets from animals may be less abundant, in particular those derived from humans. Despite these advantages, concerns about use of clonal cells have been raised. For instance, the functional interactions within an islet are lost. A specific concern in metabolic research is that in these immortalized tumor cells metabolism may primarily be devoted to cellular replication, a process which occurs at a low frequency in the postmitotic primary β-cells. To address these issues, the metabolic changes that we observed in 832/13 β-cells need to be compared to those seen in primary islets, and this will be an area for future studies.
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Figure 4. Principal component analysis of metabolite data. Score plot illustrating clustering of the samples according to cell culture conditions (A). The samples are named XYz, in which X describes glucose concentration during culturing (H ) high, L ) low), Y is the sample number, and z is the replicate number (a, b, or c). Loading plot (B) describing the contribution of the metabolites to the PCs. Abbreviations: Ala, alanine; Asp, aspartic acid; Cit, citrate; Fum, fumarate; Glc6P, glucose 6-phosphate; Glc, glucose; Gln, glutamine; Gly, glycine; Hyp, hydroxyproline; Mal, malate; Orn, ornithine; Pyr, pyruvate; Ri5P, ribose-5-phosphate; Ser, serine; Thr, threonine; Val, valine.
To compare metabolite levels, we first induced a condition in the β-cells reminiscent of glucotoxicity, through culture at a supraphysiological glucose concentration for 48 h. Indeed, we found that the insulin content of the 832/13 β-cells was decreased. Moreover, insulin secretion in response to glucose and a nonfuel secretagogue (KCl) was impaired under both KATP-dependent and KATP-independent conditions. Interestingly, in cells kept at 2.8 mM glucose, glucose-6-phosphate, the first metabolite in glycolysis produced by glucokinase, was undetectable. Under these conditions, the β-cell primarily metabolizes other fuels, for example, lipids mobilized from intracellular triglyceride stores.19 In contrast, in cells kept at 16.7 mM glucose, glucose-6-phosphate was highly abundant. This indicates that the β-cell has switched its metabolism to that of glucose, and demonstrates that our metabolomic approach has the capacity to detect changes in the metabolic state of clonal β-cells. In fact, an increase in glucose content in the extracts from cells kept at 16.7 mM glucose was also observed. Whether this truly describes intracellular glucose or glucose from the medium, which has not sufficiently been rinsed off, remains to be established. At any rate, the levels of intracellular glucose are low and transient upon elevation of extracellular glucose due to the high efficiency of glucokinase. Also, the level of pyruvate, the end-product of glycolysis, was
Figure 5. Alterations in metabolic pathways of the β-cell. Increases in metabolite levels in 832/13 β-cells cultured at 16.7 mM glucose for 48 h have been indicated by filled arrows, while decreases are shown by empty arrows. Only metabolites which were altered have been included, with the exception of metabolites which are in equilibrium with altered metabolites; these are indicated in a smaller type. Glucose conversion to pyruvate is glycolysis, while conversion of glucose-6-phosphate to ribose5-phosphate and back to glyceraldehyde-3-phosphate is part of the pentose phosphate shunt; tricarboxylic acid (TCA) cycle. Table 1. Variability of Individual 832/13 β-Cells Precultured at 16.7 mM Glucose and Reproducibility of GC-MS Analysisa
compounds
Alanine Aspartic acid Citric acid Fumaric acid Glucose-6-phosphate Glucose Glutamine Glycine Hydroxyproline Malic acid Ornithine Pyruvate Ribose-5-phosphate Serine Threonine Valine
relative CV CV abundance SD (interassay) (intra-assay) in % (n ) 3–4) in % in %
100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
15.0 28.2 17.2 23.2 63.2 7.0 11.6 13.1 11.2 20.2 36.4 11.9 30.9 15.5 13.2 13.9
15.0 28.2 17.2 23.2 63.2 7.0 11.6 13.1 11.2 20.2 36.4 11.9 30.9 15.5 13.2 13.9
25.2 10.9 16.8 13.5 18.9 3.3 8.0 16.0 8.3 7.6 9.7 12.5 11.5 21.4 7.6 23.5
a Samples of 832/13 β-cells were obtained from three to four 10 cm plates. Each sample was analyzed in triplicate by GC-MS. CV: coefficient of variation.
clearly increased. This metabolite may feed carbons into the TCA cycle via PDC or PC (anaplerosis).20 Additionally, several intermediates of the TCA cycle, that is, citrate, fumarate, and malate, were found to be increased upon culture at 16.7 mM glucose. Changes in amino acid levels were more variable. One anticipates increased protein synthesis in β-cells at elevated glucose, since it is required for biosynthesis of insulin and enhanced replication, processes which are both known to be Journal of Proteome Research • Vol. 7, No. 01, 2008 405
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Table 2. Variability of Individual 832/13 β-Cells Precultured at 2.8 mM Glucose and Reproducibility of GC-MS Analysisa
compounds
Alanine Aspartic acid Citric acid Fumaric acid Glucose-6-phosphate Glucose Glutamine Glycine Hydroxyproline Malic acid Ornithine Pyruvate Ribose-5-phosphate Serine Threonine Valine
relative CV CV abundance SD (interassay) (intra-assay) in % (n ) 3–4) in % in %
25.0 150.0 34.0 20.9 -b 41.6 172.5 139.7 79.9 13.2 262.2 -b 60.7 195.8 104.2 97.1
6.8 15.3 5.6 5.8 -b 6.6 29.5 40.1 6.7 4.7 85.3 -b 5.0 27.2 9.6 62.1
15.4 20.2 24.4 19.5 -b 19.1 19.0 19.9 9.4 25.1 22.2 -b 36.6 23.4 13.3 41.6
21.2 9.0 14.4 19.4 -b 6.9 12.4 30.2 7.7 13.4 27.4 -b 18.7 13.0 4.6 56.8
a Samples of 832/13 β-cells were obtained from three to four 10 cm plates. Each sample was analyzed in triplicate by GC-MS. Data are expressed as a percentage of the content present in β-cells cultured at 16.7 mM glucose. CV: coefficient of variation. b Metabolite not present in the samples.
stimulated by glucose. At the same time, the toxic effect of glucose impairs β-cell function.16 Indeed, this was confirmed by our proteomics data, which showed that a number of proteins was either increased or decreased in the cells at high glucose (158 versus 147 spots). Moreover, several amino acids are in equilibrium with 2-oxo acids and other fuel intermediates, via transamination and oxidation reactions. Thus, both increases and decreases may occur. One such amino acid is alanine; its level was increased, and this could be understood in light of the increased availability of TCA cycle intermediates when glucose is elevated, for example, pyruvate, which we could demonstrate here. Alanine is readily converted to pyruvate. Despite these facts, alanine does not stimulate insulin release.21 In contrast, levels of glutamine, serine, and ornithine were reduced. Glutamine is a potentially important metabolite in β-cell stimulus-secretion coupling; it is converted to glutamate and subsequently to R-ketoglutarate and can thus provide carbons to the TCA cycle in an anaplerotic fashion. The latter reaction is catalyzed by glutamate dehydrogenase (GDH), an important enzyme which has been implicated in control of insulin secretion.22 Patients with an activating mutation of GDH suffer from the hyperinsulinism/hyperammonemia syndrome in infancy (HI/HA).23 However, another line of research argues that carbons leave the TCA cycle via R-ketoglutarate to form glutamate, which may serve as a putative coupling factor in β-cells.24 Despite great effort, we were not able to identify glutamate in the extracts from 832/ 13 β-cells. The role of ornithine in pancreatic β-cells is unclear. In some metabolically active cells, for example, hepatocytes, the amino acid is an integral part of the urea cycle, where arginine is converted to ornithine with concomitant production of urea, a process which detoxifies cells from ammonia produced when proteins are degraded, particularly in the fasted state. Perhaps the increased demand for protein synthesis at high glucose results in a lower activity of the urea cycle, and hence, the level of ornithine is reduced. The aspartate level trended toward a decrease (P ) 0.057). This is of interest in view of the important role of shuttles in 406
Journal of Proteome Research • Vol. 7, No. 01, 2008
reducing equivalents in synchronizing glycolysis with the TCA cycle.25 In the malate-aspartate shuttle, the exchange of malate for aspartate over the inner mitochondrial membrane allows regeneration of NAD+ in the cytosol, which is required as a cofactor when glyceraldehyde-3 phosphate is oxidized to 1,3-bisphosphoglycerate. Concurrently, the oxidation of malate to oxaloacetate in the mitochondrial matrix produces NADH, which drives the respiratory chain. Interestingly, also glycerol3-phoshate dehydrogenase, the rate-limiting enzyme of the glycerol phosphate shuttle, was upregulated in the β-cells cultured at 16.7 mM glucose. Ribose-5-phosphate levels were also increased. This is noteworthy because activity in the pentose phosphate shunt has been reported to be increased in islets when glucose is elevated.26 PCA was performed to examine whether patterns of metabolite changes, in addition to altered levels of individual metabolites, have the potential to reflect β-cell function. Encouragingly, with PCA, a model could be made that correctly clustered the GC-MS data according to the cell culture conditions. Thus, PCA may prove to be a very powerful tool for interpretation of metabolite patterns from whole chromatograms containing several hundreds of metabolite peaks. Clearly, several important metabolites were not detected by our metabolomic approach. There may be several explanations for this. One possibility is that some metabolites are very instable and will be degraded before they can be extracted from our mammalian cells. Others may not be amenable to derivatization, using our approach. For yet others, GC-MS may not be the appropriate technique for separation and quantification. So far, while analyses of selected metabolites have been performed with a “metabolomic” approach,27 not many global analyses of islets or pancreatic β-cells have been attempted. Edwards and Kennedy used negative mode MALDI time-offlight MS to analyze metabolites extracted from pancreatic islets.28 They observed 44 metabolites, 29 of which could be identified; no quantification was made. We have also compared our findings with those of Fiehn and colleagues derived from Arabidopsis thaliana,8 since we have adapted their approach to our cells. Most glycolytic and TCA cycle intermediates, as well as amino acids, which they observe we also were able to identify. Isocitrate was described by Fiehn and colleagues; in our chromatograms, we cannot distinguish citrate from isocitrate. Moreover, R-ketoglurate was not detected by us, but is present in the chromatograms presented by Fiehn and colleagues. The reason for these discrepancies is unclear but may relate to the different types of cells that were used and/or deconvolution techniques. Perhaps levels of these, and other metabolites, are higher and more stable in plant cells than in mammalian cells. This notwithstanding, the sensitivity of our approach appears to be reasonable. As a complement to the metabolomic analysis, we also performed a global proteomic analysis of the 832/13 β-cells under conditions identical to those of the metabolomic analysis. With our approach, ∼800 spots were distinguishable on the gels, and the identity of 75 proteins whose levels were changed could be determined. Recently, a proteomic analysis of the clonal β-cell line MIN-6 at low and high passage was made.29 It was reported that 152 proteins were decreased while 59 were increased, numbers which are similar to those reported by us, although the experimental situation was quite different. Moreover, Ahmed and Bergsten identified 77 proteins in islets from C57BL/6J mice which were analyzed freshly or after culture for 24 h at 11.1 mM glucose.30 Overall, we were not able to
Omics Analysis of Clonal β-Cells
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Figure 6. Preparative gels of 832/13 β-cells that were cultured at 2.8 mM glucose (A) and 16.7 mM glucose (B) for 48 h. Approximately 450 µg of protein was loaded. Separation for the first dimension was performed on IPG strips pH 4–7, and for the second dimension continuous 12.5% T polyacrylamide gels were used. Journal of Proteome Research • Vol. 7, No. 01, 2008 407
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Table 3. Proteins Upregulated in 832/13 β-Cells Cultured at 16.7 mM Glucose versus 2.8 mM Glucose protein name
Endoplasmic reticulum protein ERp29 precursor Predicted similar to prefoldin 1 Protein disulfide-isomerase A3 precursor Protein disulfide-isomerase precursor Tubulin-specific chaperone A Actin, cytoplasmic 1 Calponin-3 Isoform 1 of tubulin beta-5 chain Keratin, type I cytoskeletal 10 Keratin, type II cytoskeletal 1 Predicted ZH14 protein Stathmin Alpha-enolase ATP synthase subunit beta, mitochondrial precursor Glycerol-3-phosphate dehydrogenase [NAD+], cytoplasmic Superoxide dismutase Transaldolase
spot no.
accession no.
Swis-Prot/ TrEMBL
gene symbol
Mascot score
tandem E-value
Chaperone 2470 IPI00207184 P52555
Erp29
336
2537 1602 1636 2516
IPI00193605 IPI00324741 IPI00198887 IPI00421723
Pfdn1 Pdia3 P4hb Tbca
198 1363 809 366
1896 2018 1977 1663 2095 2069 2124 2467
Cytoskeletal Regulation IPI00189819 P60711 Actb IPI00189819 P60711 Actb IPI00205566.1 P37397 Cnn3 IPI00197579.2 P69897 Tubb5 IPI00421806.1 Q6IFW6 Krt10 IPI00421857.1 Q6IMF3 Kb1 IPI00364093 Q1RP74 Ckap1 IPI00231697 P13668 Stmn1
77 495 477 1120 256 108 361 78
3.2 0 0 0 2.5 2.0 4.0 1.0
P11598 P04785 Q6PEC1
0
matched peaks
total intensity
fold change
18
400144.7
1.3
3.2 × 10-18 0 0 0
10 144 44 32
243091.7 2861253.3 873800.7 648959.0
1.4 1.5 1.6 1.2
× 10-16
2 26 24 86 10 4 22 4
17772.7 806292.3 700967.0 2099833.0 64781.5 33575.4 464459.9 25158.4
1.2 1.1 1.9 2.0 2.2 NAa 1.1 5.4
× × × ×
10-17 10-9 10-41 10-4
Metabolism 1748 IPI00464815.1 P04764 1729 IPI00551812 P10719
Eno1 Atp5b
681 158
0 1.3 × 10-9
42 14
925878.3 63751.4
2.1 2.0
2117 IPI00231148
Gpd1
369
0
28
188639.2
1.9
Sod1 Taldo1
245 579
6.3 × 10-36 0
12 62
233479.1 1053817.5
1.5 1.2
202 86 1542 208
1.0 × 10-10 4.0 × 10-10 0 1.3 × 10-39
12 8 130 18
266363.5 33154.0 2660940.1 225453.2
2.1 1.7 1.7 1.5
O35077
2497 IPI00231643.5 P07632 2016 IPI00190377 Q9EQS0 Proliferation IPI00197553.1 P13084 IPI00363265 P48721 IPI00363265 P48721 IPI00210011.2 Q63528
Isoform B23.1 of nucleophosmin Predicted stress-70 protein, mitochondrial precursor Replication–protein A 32 kDa subunit
2035 1498 1491 2228
Proteasome subunit alpha type 3 Proteasome subunit beta type 7 precursor
Protein Degradation 2272 IPI00476178 P18422 Psma3 2283 IPI00199980 Q9JHW0 Psmb7
261 359
5.0 × 10-44 0
18 28
270255.1 501383.9
NAa 1.2
Protein Synthesis 2260 IPI00471525.2 Q68FR9 Eef1d
473
0
18
364496.6
NAa
Eukaryotic translation elongation factor 1 delta Eukaryotic translation initiation factor 4A1 Eukaryotic translation initiation factor 4E Eukaryotic translation initiation factor 4H Similar to glycyl-tRNA synthetase Proliferating cell nuclear antigen 14-3-3 protein gamma Fatty acid-binding protein, heart Isoform 2 of phosphatidylinositol transfer protein beta isoform Predicted cellular retinoic acid-binding protein 1 Peroxiredoxin-2 Predicted adenine phosphoribosyltransferase Predicted similar to histone H2B 291B Predicted similar to huntingtin interacting protein 2 Predicted similar to programmed cell death protein 5 Ribosomal protein, mitochondrial, L12 Splicing factor 3a, subunit 3 Similar to uroporphyrinogen decarboxylase a
408
1806 2326 2234 1476
Eif4a1 Eif4e Wbscr1 Gars
439 234 357 1062
0 1.3 × 10-29 0 0
28 16 18 54
263376.6 489336.2 306476.5 1026783.7
1.6 1.3 1.7 2.0
Signaling 2160 IPI00200861 P04961 2310 IPI00230835 P61983
Pcna Ywhag
376 159
6.3 × 10-41 1.3 × 10-11
20 10
239885.7 50960.9
NAa NAa
Transport 2545 IPI00231971 P07483 2159 IPI00551671.3 P53812
Fabp3 Pitpnb
318 465
1.4 × 10-45 0
22 38
288884.8 470842.4
2.6 1.4
2529 IPI00369689
Crabp1
222
7.9 × 10-23
12
123598.8
3.6
Prdx2 Aprt Hist1h2bc Hip2
235 242 226 302
0 0 7.9 × 10-26 1.4 ×10-45
28 20 8 20
564369.4 204801.5 280237.1 216824.1
1.1 3.5 3.3 1.2
2521 IPI00193547
Pdcd5
374
0
18
654852.0
1.6
2440 IPI00203773 1616 IPI00565197.1 Q4KLI7 1972 IPI00215258.3
Mrpl12 Sf3a3 Urod
273 339 357
7.9 × 10-28 0 0
8 20 20
224195.8 166098.0 325241.3
1.5 1.2 1.8
2410 2409 2510 2388
IPI00369618 IPI00199271 IPI00370315.1 IPI00364262.3
Q6P3V8 P63074 Q5XI72 Q5I0G4
Npm1 n Hspa9a Hspa9a Rpa2
P62966
Others IPI00201561 P35704 IPI00555297 P36972 IPI00480852.2 IPI00361820
NA indicates that the given spot was only present in 832/13 β-cells cultured at 16.7 mM glucose.
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Omics Analysis of Clonal β-Cells Table 4. Proteins Downregulated in 832/13 β-Cells Cultured at 16.7 versus 2.8 mM Glucose protein name
78 kDa glucose-regulated protein precursor BWK4 Heat shock protein HSP 90-beta Protein disulfide-isomerase A3 precursor Protein disulfide-isomerase precursor
Isoform 1 of Tubulin beta-5 chain
Microtubule-associated protein RP/EB family member 3 Profilin 2 Similar to beta tubulin 1, class VI Tubulin alpha-1 chain Tubulin beta-2C chain ATP synthase subunit beta, mitochondrial precursor Isovaleryl-CoA dehydrogenase, mitochondrial precursor Phosphoglycerate mutase 1 Predicted similar to 6phosphogluconolactonase Predicted similar to glyceraldehyde-3phosphate dehydrogenase Sulfite oxidase Cystatin-B Heterogeneous nuclear ribonucleoprotein F Heterogeneous nuclear ribonucleoprotein K Prohibitin Nonspecific dipeptidase Ubiquitin carboxyl-terminal hydrolase isozyme L1 Eukaryotic translation initiation factor 3, subunit 4 Eukaryotic translation initiation factor 5A-1 Mitochondrial-processing peptidase subunit beta, mitochondrial precursor Immunophilin XAP2 Isoform 1 of Growth factor receptorbound protein 2 Chloride intracellular channel 1 Cytochrome c oxidase subunit 5A, mitochondrial precursor Retinol-binding protein I, cellular Transitional endoplasmic reticulum ATPase Chromogranin A precursor
spot no.
accession no.
Swiss-Prot/ TrEMBL
gene symbol
Mascot score
Chaperone P06761 Hspa5
1254 IPI00206624
939
tandem E-value
matched peaks
total intensity
fold change
0
70
836167.0
0.5
1816 1630 1379 1948 1957
IPI00364866 IPI00471584 IPI00324741 IPI00198887 IPI00198887
Txndc4 Hspcb Pdia3 P4hb P4hb
122 618 1031 306 260
1.3 × 10-12 0 0 0 6.3 × 10-25
8 38 52 18 10
33966.8 823796.0 784286.9 643367.8 117891.9
0.4 0.6 0.9 0.6 0.6
1618 1776 2074 2214 1836
Cytoskeletal Regulation IPI00197579.2 P69897 Tubb5 IPI00197579.2 P69897 Tubb5 IPI00197579.2 P69897 Tubb5 IPI00197579.2 P69897 Tubb5 IPI00360288 Q5XIT1 Mapre3
627 437 173 374 237
0 0 7.9 × 10-39 0 1,6E-22
34 24 14 30 14
423788.7 233082.6 144772.4 205561.7 172572.1
0.4 0.3 0.4 0.3 0.6
2131 1939 1759 1794 2215
IPI00231920 IPI00768167 IPI00189795 IPI00189795 IPI00400573.1
160
5.0 6.3 1.0 6.3
10-13 10-3 10-11 10-19
8 1 8 8 24
70825.1 358.7 48221.9 41474.9 146171.8
0.8 0.3 0.8 0.7 0.3
145
3.2 × 10-41
8
100381.5
0.6
2005 IPI00551812
Q5VLR5 P34058 P11598 P04785 P04785
Q9EPC6 P68370 P68370 Q6P9T8
Pfn2 LOC679312 Tuba1 Tuba1 Tubb2c
Metabolism P10719 Atp5b
110 104 268
× × × ×
1639 IPI00193716
P12007
Ivd
505
0
30
567767.1
0.6
2127 IPI00421428 1946 IPI00362469
P25113
Pgam1 Pgls
73 396
1.6 × 10-5 0
4 16
36261.6 248290.1
0.5 0.7
RGD1565368
204
1.6 × 10-36
8
107504.8
0.1
Suox
541
0
24
247194.4
0.5
108
2.0 × 10-10 2.0 × 10-3
12 1
193836.3 5134.2
0.6 0.6
2163 IPI00554039.1 1397 IPI00193919 2135 IPI00209848 1899 IPI00210357
Q07116
Proliferation P01041 Cstb Q794E4 Hnrpf
1326 IPI00194974.2 P61980
Hnrpk
749
0
48
625457.8
0.6
1923 IPI00211756
Phb
550
0
24
613102.6
0.8
Protein Degradation 1451 IPI00421899.1 Q6Q0N1 Cndp2 2053 IPI00204375.2 Q00981 Uchl1
P67779
801 126
0 0
36 8
685720.7 144798.2
0.8 0.5
Protein Synthesis 1569 IPI00365487 Q5RK09 Eif3s4
364
0
18
210758.4
0.6
2092 IPI00211216
Q3T1J1
Eif5a
216
0
14
188259.2
0.5
1529 IPI00209980
Q03346
Pmpcb
523
0
22
212379.5
0.4
Aip Grb2
196 452
4.0 × 10-27 0
14 18
73007.6 465081.8
0.6 0.6
Clic1 Cox5a
226 88
4.0 × 10-28 6.3 × 10-10
14 4
93129.5 35552.3
0.9 0.5
2126 IPI00231825 P02696 1800 IPI00212014.2 P46462
Rbp1 Vcp
308 287
5.0 × 10-37 1.3 × 10-42
22 16
242892.9 527440.8
0.7 0.8
Others 2066 IPI00189627.1 P10354
Chga
54
2
96751.4
0.3
Signaling 1763 IPI00192541 Q5FWY5 2001 IPI00203630.1 P62994
1897 IPI00421995 2134 IPI00192246
Transport Q6MG61 P11240
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Table 4. Continued protein name
COP9 (Constitutive photomorphogenic) homologue, subunit 5 Ectonucleotide pyrophosphatase/ phosphodiesterase family member 5 Nucleoside diphosphate kinase A Phenazine biosynthesis-like domain-containing protein Secretagogin Thioredoxin
spot no.
accession no.
gene symbol
Mascot score
tandem E-value
matched peaks
total intensity
fold change
1728
IPI00366535
Q4KM69
Cops5
224
4.0 × 10-30
20
161187.2
0.6
1446
IPI00454558
P84039
Enpp5
106
2.5 × 10-3
8
49040.3
0.5
2088 1931
IPI00194404 IPI00200041
Q05982 Q68G31
Nme1 Mawbp
478 497
0 0
30 24
623618.1 318230.0
0.7 0.8
1938 2138
IPI00400628.1 IPI00231368
Q6R556 P11232
Scgn Txn1
188 214
1.0 × 10-5 6.3 × 10-25
8 20
79185.0 267173.8
0.7 1.0
extensively correlate the changes in protein levels with the metabolic alterations seen in our cells. Proliferating cell nuclear antigen was upregulated upon cell culture at high glucose, which agrees with the increased replication triggered by glucose. The only metabolic enzymes that were found to be upregulated were glycerol-3-phosphate dehydrogenase, a key enzyme in the glycerol phosphate shuttle, R-enolase, a glycolytic enzyme which also was upregulated in islets cultured at 11.1 mM glucose,30 and transaldolase, an enzyme in the pentose phosphate shunt. These changes agree with the observed increase in levels of glycolytic intermediates and ribose-5-phosphate, deriving from the pentose phosphate shunt. Surprisingly, a protein predicted to be similar to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was downregulated. This does not agree with increased glycolytic flux. Also, GAPDH mRNA levels are often used for normalization of expression data; whether the mRNA levels of the protein were regulated similarly in our cells was not examined. Phosphoglycerate mutase 1, another glycolytic enzyme was actually downregulated in the β-cells precultured at high glucose. Thus, it is most striking that there appears to be little correlation between increases in metabolite levels and expression of the regulatory enzymes. This suggests that substrate availability is a more important determinant of metabolite levels than enzyme expression at the protein level. Additionally, posttranslational modifications and allosteric regulation of the enzymes are likely to play a role. We here report a technical variation of below 25% of the mean, while others report 2%.31 This discrepancy can be explained by differences in measuring the technical variation. Here, the technical variation was calculated as the mean of the coefficient of variation of triplicates obtained from the 3–4 samples analyzed by GC-MS for each glucose condition, while Roessner and colleagues31 used a unique sample that was repetitively analyzed 15 times. The total variation in our experiments, to which the biological variability contributes, is apparent from the standard deviations of the metabolite levels.
Conclusion The present work offers a proof-of-concept, where we are able to detect clear changes in metabolite levels when clonal β-cells were cultured under different conditions, in this case, a model for glucotoxicity. Indeed, the metabolomic analysis holds promise to become an important tool for screening and analysis of cellular metabolism. However, while the metabolome in β-cells described here agrees with what would be expected when there is increased availability of glucose for cells to metabolize, the metabolomic comparisons in the present work are correlative with respect to β-cell dysfunction. To establish that metabolic changes are in fact pathogenic, 410
Swiss-Prot/ TrEMBL
Journal of Proteome Research • Vol. 7, No. 01, 2008
manipulation of candidate pathways, for instance using RNA interference for knock down of regulatory metabolic enzymes, is required. Such studies are underway in our laboratory. Another realization is that increased metabolism of glucose, as seen in cells cultured at 16.7 mM glucose, does not necessarily imply enhanced stimulus-secretion coupling. Clearly, it is the precise manner in which glucose is metabolized in the β-cell, not the extent of metabolism, which determines insulin secretion.2
Acknowledgment. The studies were supported by the Swedish Research Council (H.M., 14196-06-3; C.H., 1128413-3), The European Foundation for the Study of Diabetes/ MSD (H.M.), the Crafoord and Albert Påhlsson Foundations, the Swedish Diabetes Association, and the Faculty of Medicine at Lund University. C.F. was supported by the National Research School in Genomics and Bioinformatics through a grant to C.H. and P.J.; M.K. was supported by the Swedish Foundation for Strategic Research and the Knut and Alice Wallenberg Foundation through the Swegene consortium and the Strategic Science Foundation (SSF) CREATE Health centre. We thank Mats Mågård, Ulrika Brynnel, and Liselotte Andersson for excellent technical assistance. We also thank Dr. Peter Osmark for fruitful discussion regarding GC-MS analysis. References (1) Wollheim, C. B. Beta-cell mitochondria in the regulation of insulin secretion: a new culprit in type II diabetes. Diabetologia 2000, 43 (3), 265–277. (2) Lu, D.; Mulder, H.; Zhao, P.; Burgess, S. C.; Jensen, M. V.; Kamzolova, S.; Newgard, C. B.; Sherry, A. D. 13C NMR isotopomer analysis reveals a connection between pyruvate cycling and glucose-stimulated insulin secretion (GSIS). Proc. Natl. Acad. Sci. U.S.A. 2002, 99 (5), 2708–2713. (3) Fahien, L. A.; MacDonald, M. J. The succinate mechanism of insulin release. Diabetes 2002, 51 (9), 2669–2676. (4) Khan, A.; Ling, Z. C.; Landau, B. R. Quantifying the carboxylation of pyruvate in pancreatic islets. J. Biol. Chem. 1996, 271 (5), 2539– 2542. (5) Fransson, U.; Rosengren, A. H.; Schuit, F. C.; Renström, E.; Mulder, H. Anaplerosis via pyruvate carboxylase is required for the fuelinduced rise in the ATP:ADP ratio in rat pancreatic islets. Diabetologia 2006, 49 (7), 1578–1586. (6) Henquin, J. C. Triggering and amplifying pathways of regulation of insulin secretion by glucose. Diabetes 2000, 49 (11), 1751–1760. (7) Dunn, W. B.; Bailey, N. J.; Johnson, H. E. Measuring the metabolome: current analytical technologies. Analyst 2005, 130 (5), 606– 625. (8) Fiehn, O.; Kopka, J.; Dormann, P.; Altmann, T.; Trethewey, R. N.; Willmitzer, L. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 2000, 18 (11), 1157–1161. (9) Hohmeier, H. E.; Mulder, H.; Chen, G.; Henkel-Rieger, R.; Prentki, M.; Newgard, C. B. Isolation of INS-1-derived cell lines with robust ATP-sensitive K+ channel-dependent and -independent glucosestimulated insulin secretion. Diabetes 2000, 49, 424–430.
research articles
Omics Analysis of Clonal β-Cells (10) Fiehn, O.; Kopka, J.; Trethewey, R. N.; Willmitzer, L. Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry. Anal. Chem. 2000, 72, 3573–3580. (11) Gorg, A.; Obermaier, C.; Boguth, G.; Harder, A.; Scheibe, B.; Wildgruber, R.; Weiss, W. The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 2000, 21 (6), 1037–1053. (12) Lamanda, A.; Zahn, A.; Roder, D.; Langen, H. Improved Ruthenium II tris (bathophenantroline disulfonate) staining and destaining protocol for a better signal-to-background ratio and improved baseline resolution. Proteomics 2004, 4 (3), 599–608. (13) Ihaka, R.; Gentleman, R. R. A language for data analysis and graphics. J. Comput. Graph. Stat. 1996, 5, 299–314. (14) Arrigoni, G.; Fernandez, C.; Holm, C.; Scigelova, M.; James, P. Comparison of MS/MS methods for protein identification from 2D-PAGE. J. Proteome Res. 2006, 5 (9), 2294–2300. (15) Levander, F.; Krogh, M.; Warell, K.; Garden, P.; James, P.; Hakkinen, J. Automated reporting from gel-based proteomics experiments using the open source Proteios database application. Proteomics 2007, 7 (5), 668–674. (16) Poitout, V.; Robertson, R. P. Minireview: Secondary beta-cell failure in type 2 diabetes–a convergence of glucotoxicity and lipotoxicity. Endocrinology 2002, 143 (2), 339–342. (17) Roche, E.; Assimacopoulos-Jeannet, F.; Witters, L. A.; Perruchoud, B.; Yaney, G.; Corkey, B.; Asfari, M.; Prentki, M. Induction by glucose of genes coding for glycolytic enzymes in a pancreatic beta-cell line (INS-1). J. Biol. Chem. 1997, 272 (5), 3091–3098. (18) Asfari, M.; Janjic, D.; Meda, P.; Li, G.; Halban, P. A.; Wollheim, C. B. Establishment of 2-mercaptoethanol-dependent differentiated insulin-secreting cell lines. Endocrinology 1992, 130 (1), 167– 178. (19) Berne, C. The metabolism of lipids in mouse pancreatic islets: the biosynthesis of triacylglycerols and phospholipids. Biochem. J. 1975, 152, 667–673. (20) MacDonald, M. J.; Fahien, L. A.; Brown, L. J.; Hasan, N. M.; Buss, J. D.; Kendrick, M. A. Perspective: emerging evidence for signaling roles of mitochondrial anaplerotic products in insulin secretion. Am. J. Physiol.: Endocrinol. Metab. 2005, 288 (1), E1–15. (21) Lernmark, A. Specificity of leucine stimulation of insulin release. Hormones 1972, 3 (1), 14–21. (22) Maechler, P. Novel regulation of insulin secretion: the role of mitochondria. Curr. Opin. Invest. Drugs 2003, 4 (10), 1166–1172.
(23) Stanley, C. A.; Lieu, Y. K.; Hsu, B. Y.; Burlina, A. B.; Greenberg, C. R.; Hopwood, N. J.; Perlman, K.; Rich, B. H.; Zammarchi, E.; Poncz, M. Hyperinsulinism and hyperammonemia in infants with regulatory mutations of the glutamate dehydrogenase gene. N. Engl. J. Med. 1998, 338 (19), 1352–1357. (24) Maechler, P.; Wollheim, C. B. Mitochondrial glutamate acts as a messenger in glucose-induced insulin exocytosis [see comments]. Nature 1999, 402 (6762), 685–689. (25) Eto, K.; Tsubamoto, Y.; Terauchi, Y.; Sugiyama, T.; Kishimoto, T.; Takahashi, N.; Yamauchi, N.; Kubota, N.; Murayama, S.; Aizawa, T.; Akanuma, Y.; Aizawa, S.; Kasai, H.; Yazaki, Y.; Kadowaki, T. Role of NADH shuttle system in glucose-induced activation of mitochondrial metabolism and insulin secretion [see comments]. Science 1999, 283 (5404), 981–985. (26) Ammon, H. P.; Patel, T. N.; Steinke, J. The role of the pentose phosphate shunt in glucose induced insulin release: in vitro studies with 6-aminonicotinamide, methylene blue, NAD +, NADH, NADP +, NADPH and nicotinamide on isolated pancreatic rat islets. Biochim. Biophys. Acta 1973, 297 (2), 352–367. (27) Jensen, M. V.; Joseph, J. W.; Ilkayeva, O.; Burgess, S.; Lu, D.; Ronnebaum, S. M.; Odegaard, M.; Becker, T. C.; Sherry, A. D.; Newgard, C. B. Compensatory responses to pyruvate carboxylase suppression in islet beta-cells. Preservation of glucose-stimulated insulin secretion. J. Biol. Chem. 2006, 281 (31), 22342–22351. (28) Edwards, J. L.; Kennedy, R. T. Metabolomic analysis of eukaryotic tissue and prokaryotes using negative mode MALDI time-of-flight mass spectrometry. Anal. Chem. 2005, 77 (7), 2201–2209. (29) Dowling, P.; O’Driscoll, L.; O’Sullivan, F.; Dowd, A.; Henry, M.; Jeppesen, P. B.; Meleady, P.; Clynes, M. Proteomic screening of glucose-responsive and glucose non-responsive MIN-6 beta cells reveals differential expression of proteins involved in protein folding, secretion and oxidative stress. Proteomics 2006, 6 (24), 6578–6587. (30) Ahmed, M.; Bergsten, P. Glucose-induced changes of multiple mouse islet proteins analysed by two-dimensional gel electrophoresis and mass spectrometry. Diabetologia 2005, 48 (3), 477– 485. (31) Roessner, U.; Wagner, C.; Kopka, J.; Trethewey, R. N.; Willmitzer, L. Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J. 2000, 23 (1), 131–142.
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