Quantitative Phosphoproteomics Revealed Glucose-Stimulated

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Quantitative Phosphoproteomics Revealed Glucosestimulated Responses of Islet Associated with Insulin Secretion Jiaming Li, Qingrun Li, Jiashu Tang, Fangying Xia, Jiarui Wu, and Rong Zeng J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00507 • Publication Date (Web): 05 Oct 2015 Downloaded from http://pubs.acs.org on October 10, 2015

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Journal of Proteome Research

Quantitative Phosphoproteomics Revealed Glucosestimulated Responses of Islet Associated with Insulin Secretion Jiaming Li1†, Qingrun Li1†, Jiashu Tang1, Fangying Xia1, Jiarui Wu1,2,3*, Rong Zeng1,2,3* 1

Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai

Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China 2

Department of Life Sciences, ShanghaiTech University, 100 Haike Road, Shanghai, 201210,

China 3

Shanghai Institutes for Advanced Study, Chinese Academy of Sciences, 99 Haike Road,

Shanghai, 201210, China

ACS Paragon Plus Environment

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ABSTRACT

As central tissue of glucose homeostasis, islet has been an important focus of diabetes research. Phosphorylation plays pivotal roles in islet function, especially in islet glucose-stimulated insulin secretion (GSIS). A systematic view on how phosphorylation networks were coordinately regulated in this process remains lacking, partially due to the limited amount of islets from an individual for a phosphoproteomic analysis. Here, we optimized the in-tip and best-ratio phosphopeptide enrichment strategy and a SILAC-based workflow for processing rat islet samples. With limited islet lysates from each individual rat (20 µg - 47 µg), we identified 8,539 phosphosites on 2,487 proteins. Subsequent quantitative analyses uncovered that short-term (30 min) high glucose stimulation induced coordinate responses of islet phosphoproteome on multiple biological levels, including insulin secretion related pathways, cytoskeleton dynamics, protein processing in ER and Golgi, transcription and translation, etc. Furthermore, three glucose-responsive phosphosites (Prkar1a pT75pS77 and Tagln2 pS163) from the dataset were proved to be correlated with insulin secretion. Overall, we initially gave an in-depth map of islet phosphoproteome regulated by glucose on individual rat level. This was a significant addition to our knowledge about how phosphorylation networks responded in insulin secretion. Also, the list of changed phosphosites was a valuable resource for molecular researchers in diabetes field.

KEYWORDS: glucose-stimulated insulin secretion, islet, mass spectrometry, phosphorylation, SILAC


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Introduction Pancreatic islet is an important specialized endocrine tissue continuously sensing the levels of blood glucose and, in response, secreting insulin to maintain normal fuel homeostasis. Absolute insulin deficiency caused by pancreatic islet dysfunction is the key step in pathogenesis of type 2 diabetes, given that diabetes will only arise when islet beta cells are no longer able to produce sufficient insulin for the increasing demand (1). A detailed knowledge of molecules and signaling pathways associated with normal islet function (e.g., GSIS), or islet dysfunction in diabetic subjects will promote our understanding of basic islet biology and benefit the development of new therapeutic strategies for diabetes. When uncovering relationship between phenotypes and mechanisms, high-throughput robust comparative omics strategy provides much higher efficiency in targeting potential key molecular factors. With the rapid development of mass spectrometry (MS) technology, previous large-scale efforts have helped to define the islet proteome and revealed its variability across chronic high glucose treatment, or during the development of diabetes or fat (2, 3). These works have advanced our understanding of basic islet biology and catalyzed the discovery of proteins involved in islet dysfunction. However, mounting evidence indicates that a range of post-translational modifications, especially phosphorylation, play critical roles in islet function as well (4, 5). Particularly, in normal islets the response of GSIS can be triggered very fast within a few minutes and maintained within an hour (6). This rapid process should rely more on protein signaling network remodeling than protein turnover. To our knowledge, so far only two papers performed qualitative phosphoproteomic analysis on INS-1 beta cell lines, with 2,467 phosphosites identified in INS-1 whole cell lysates (7) and 84 phosphosites identified in INS-1 mitochondria (8). Till now nearly no studies have investigated the phosphodynamics in GSIS on


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islet level, which could better reflect the bona fide state of beta cell physiology. Islets are scattered throughout the pancreas and comprise extremely low amount of the total pancreas mass. Compared with hundreds of micrograms to several milligrams sample requirement in conventional phosphoproteomic research, islets isolated and handpicked from an individual rat are too limited (less than 100 µg in this work). This further adds to the difficulties in islet sample analysis. In-depth islet phosphoproteomic information is still lacking. It remains a hard nut for molecular biologists in islet field to benefit from high-throughput and robust quantitative MS. Here, our work initially approached as low as 20 µg - 47 µg islet samples for phosphoproteomic analysis and combined optimization of peptide-to-TiO2 beads ratio and in-tip phosphopeptide enrichment for manipulating such low amount of islet samples. Combining with stable-isotope labeled INS-1E cells as internal standard (9), we firstly managed an effective way to perform quantitative phosphoproteomic analysis on islets from individual rat, and revealed a reliable subset of phosphosites altered by 30 min glucose stimulation. Functional validation confirmed the correlation between several phosphorylation sites (Prkar1a pT75pS77 and Tagln2 pS163) from the dataset and GSIS, suggesting the dataset a valuable resource for further investigating islet function related signaling pathways. The workflow is also of great potential in analyzing phosphoproteome of isolated islet under various conditions (drug treatment, diabetes, etc.), thus from a novel perspective, helping to expand our understanding in islet physiology, find potential key molecules in islet function, etc. Experimental Section Stable-isotope labeling with amino acids (SILAC) of INS-1E cells and preparation of internal standard


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To generate SILAC-labeled internal standards, we cultured INS-1E cells (10) in a humidified atmosphere containing 5% CO2 in SILAC medium. The SILAC medium containing isotopelabeled 13C614N4-L-Arg (Sigma, Cat. No. 608033) and 13C615N2-L-Lys (Sigma, Cat. No. 608041) was homemade referring to RPMI 1640 recipe (GIBCO, Cat. No. 11875-119) by reducing arginine concentration to 25.37 mg/L, increasing proline concentration to 200 mg/L and supplementing 10% dialyzed fetal bovine serum (FBS) (Biochrome, Cat. No. SZ0115), 1 mM sodium pyruvate, 50 µM 2-mercaptoethanol, 2 mM glutamine, 10 mM HEPES, and 100 U/ml penicillin-streptomycin. After 8 passages labeling, the cells were washed three times with PBS and collected in SDT buffer (4% SDS, 100 mM Tris-HCl, 100 mM DTT, pH 7.6). After 5 min of boiling and sonication, the samples were centrifuged at 13,000 g at room temperature for 10 min. The supernatant was collected as internal standard. Internal standard lysate alone was analyzed with LTQ-Orbitrap (Thermo Fisher Scientific) to check labeling efficiency and arginine-toproline conversion (11). Internal standard lysate were mixed with equal amount of normal INS1E (cultured with normal RPMI160 medium) lysate for LTQ-Orbitrap analysis to check the distributions of peptide ratios between normal INS-1E and internal standard with and without proline. For phosphoproteomic quantification of islet treated with high glucose, equal amounts of protein from SILAC-labeled INS-1E cells that were treated with high or low glucose (the same treatment to islets, as is descried below) were mixed. Islet isolation and glucose treatment About 6-week-old male Wistar rats were purchased from Shanghai Laboratory Animal Center (Jiu-Ting, Shanghai, China). The rats were housed in laboratory cages at a temperature of (23±2)°C and a humidity of (70±20)% under a 12 h dark/light cycle (Animal Qualification Certificate No.: SCXK hu (Shanghai) 2012-0002). The study involved animal experiments that


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were conducted ethically in accordance with guidelines of the Chinese Academy of Sciences for the use and care of animals. Islet isolation procedure follows the previously published method with slight modifications (12). Briefly, pancreas was distended with cold hanks buffer (Invitrogen, Cat. No. 14065) containing 1.0 mg/ml of collagenase P (Roche, Cat. No. 11213873001), excised, and incubated in a stationary water bath at 37°C for 12 min - 18 min. The digestion was stopped by adding cold hanks buffer supplemented with 10% FBS, followed by centrifugation at 250 g for 3 min. Islets were separated by density gradients (25%, 23%, 20% Ficoll 400 (GE Healthcare, Cat. No. 17030050) in hanks buffer, 800 g for 20 min) and handpicked under a stereomicroscope. After isolation, islets from a single rat were divided into two equal aliquots after being equilibrated with KRBH buffer (Krebs-Ringer bicarbonate buffer: 135 mM NaCl, 3.6 mM KCl, 5 mM NaHCO3, 0.5 mM NaH2PO4, 0.5 mM MgCl2, 1.5 mM CaCl2, 10 mM HEPES, 0.1% BSA, pH 7.4) containing 2.8 mM glucose for 30 min and then incubated with 2.8 mM glucose or 16.7 mM glucose KRBH buffer for 30 min, respectively. Islets were washed with PBS and boiled in SDT buffer to collected protein lysate. The protein concentration was measured as described before (11, 13). Briefly, ~2 µl islet protein was mixed with 350 µl 8 M urea. Fluorescence was measured at excitation and emission wavelength of 295 nm and 350 nm, respectively. Tryptophan was used as a standard. The protein concentration was calculated assuming a mean tryptophan content in proteins of 1.3%. Islet functional integrity test Islets of equal size were handpicked and insulin secretion was measured from groups of 20 islets treated with 150 µl KRBH buffer containing 2.8 mM or 16.7 mM glucose for 30 min after 30 min equilibrium with 2.8 mM glucose KRBH buffer. Insulin concentrations were measured with


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Mercodia ultrasensitive insulin ELISA kit (Mercodia, Cat. No. 10125101) following standard protocol. Phosphoproteomic sample preparation Islet proteins were mixed with equal amount of internal standard proteins. For the two aliquots of islets from one rat, equal amount of islet proteins was used. The samples were loaded onto PALL 10 K OMEGA filter (Cat. No. OD010C35) and processed according to filter-aided sample preparation (FASP) protocol (14) with trypsin (Promega, Cat. No. V5113). Phosphopeptide enrichment was performed using TiO2 beads (GL sciences, Cat. No. 502075000). For optimization of peptide-to-TiO2 beads ratio, heavy-labeled internal standard peptides corresponding to 100 µg protein were used. In short, the peptides were reconstituted in loading buffer (65% acetonitrile/2% TFA, saturated by glutamic acid) and incubated with TiO2 beads (peptide-to-TiO2 beads ratio = 1:4, 1:2, 1:1, 2:1, 4:1, respectively) for 10 min. Then the slurry was transferred to equilibrated C8 StageTips (Empore™ C8 47 mm Disk, 3M, Cat. No. 98060402140) and washed with wash buffer 1 (65% acetonitrile/0.5% TFA/H2O) twice and wash buffer 2 (65% acetonitrile/0.1% TFA/H2O) twice. The phosphopeptides were then eluted from the beads with elute buffer (500 mM NH4OH/60% acetonitrile). The elutes were dried down and reconstituted in 0.1% FA/H2O for LC-MS/MS analyses. Finally, peptide-to-TiO2 beads ratio 1:2 was chosen to perform phosphopeptide enrichment. LC-MS/MS analysis For all samples, online reverse-phase chromatography was performed on an UltiMate 3000 RSLC nanoLC Systems (Dionex, now ThermoFisher Scientific) and nanoflow HPLC Easy nLC 1000 system (Thermo Fisher Scientific). Peptides were separated on a 15 cm column (i.d. 75 µm) packed in-house with the reverse-phase (RP) materials ReproSil-Pur C18-AQ, 3.0 µm resin (Dr.


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Maisch GmbH, Germany) using a 113 min at 200 nL/min (for LTQ-Orbitrap Velos) or 106 min at 300 nL/min (for Q-Exactive) linear gradient from 4% to 23% of acetonitrile with 0.1% (v/v) formic acid. All mass spectral data were acquired on LTQ-Orbitrap Velos and Q-Exactive mass spectrometers (Thermo Fisher Scientific). Data were acquired using Xcalibur software. See supplementary text for details. Database searching and MS data analysis Peak list generation, estimation of false discovery rates, peptide to protein group assembly, quantification and phosphorylation site localization were performed with MaxQuant (version 1.0.14.10) as described previously (15, 16). MS/MS spectra were searched against the decoy International Protein Index (IPI) rat database version 3.77 (39489 items) containing both forward and reverse protein sequences by the Mascot search engine (Matrix Science, version 2.2). The precursor ion tolerance was set at 7 p.p.m. Fragment ions were searched with maximal mass deviation of 0.5 Da for CID data and 0.02 Da for HCD data. The search included variable modifications of phosphor (STY), methionine oxidation, protein N-terminal acetylation and fixed modification of cysteine carbamidomethylation. Peptides of minimum 6 amino acids, maximum of 2 missed cleavages and Mascot score greater than or equal to 25 were allowed. False discovery rate (FDR) was set to 0.01 for peptide, phosphorylation site and protein identification. Only class I phosphorylation sites were used for further analyses. Ratios were firstly normalized such that the median ratio was 1 to correct for unequal sample mixing (17). The ratios were then log2-transformed to make the distribution roughly symmetric and close to normal before statistical tests (18). Phosphosites with high/low glucose ratios (from 2 dependent RAW files) in at least 3 rats (from 6 RAW files totally) were designated as quantified. Phosphosites whose abundance was down-regulated more than 0.95-fold and up-regulated less than 1.05-fold were


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ignored to increase statistical sensitivity (18). At a confidence level of 0.05 (two-tailed paired t test), the maximum FDR calculated with the R package “qvalue” is 5.22% (19, 20). A fold change cutoff of 1.3 and 0.77 was further applied to minimize the effects of stochastic technical and biological variation. Finally we got 170 changed phosphosites. Statistical analyses were mainly executed with R package or Excel. Computational analysis GO analysis of biological pathway and protein class was performed using PANTHER (21). Phosphorylation motifs were extracted from class I sites with the Motif-X algorithm, with a 13 amino acids sequence windows, rat proteome as background, P value ≤ 10-6, fold increase ≥ 2, and an occurrence limit of at least 2% of the number of sequence windows submitted to the analysis (22). GPS2.1 was used to predict potential kinases for phosphorylation site (threshold: high) (23). Kinases predicted to phosphorylate less than 10% of the sites matching the motifs were discarded. Fractions of motifs matched to the kinase (%) were illustrated in the heatmap. Relative kinase activity was expressed as average fold change (log2-transformed) of the phosphosite matching the kinase (24). Validation of Prkar1a pS77 and Tagln2 pS163 SILAC data INS-1E cells were equilibrated with 2.8 mM glucose KRBH buffer for 30 min, and then treated with 2.8 mM glucose or 16.7 mM glucose for 30 min, respectively. Cell lysates were collected with SDT buffer for western blot validation of Prkar1a pS77 up-regulation and SRM validation of Tagln2 pS163 down-regulation. For Tagln2 pS163, whose phosphorylation site-specific antibody was commercially unavailable, we validated the down-regulation of the site by synthetic heavily labeled phosphorylated peptide “NFS(ph)DNQIQEGK[13C615N2]” (JPT Peptide Technologies GmbH, Germany) and SRM. A triple quadrupole MS (TSQ Vantage,


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Thermo Fisher Scientific) was used to acquire SRM data. The RAW files were processed by an in-house software SRM builder (25). See supplementary text for details. SiRNA, plasmids and cell culture For tagln2 knockdown, siRNAs (negative control siRNA, tagln2 siRNA 355, tagln2 siRNA767) were delivered into INS-1E cells using lipo 2000 (Invitrogen, Cat. No. 11668-019) at a final concentration of 90 nM. GSIS phenotypes were analyzed 72 hours after transfection. The cells were balanced with KRBH buffer containing 2.8 mM glucose and then treated with KRBH buffer containing 2.8 mM or 16.7 mM glucose for an hour. The insulin secreted was collected and measured as described above. The cell lysates were then collected with SDT buffer and the protein content was measure as described before. The insulin secreted was firstly normalized by the protein content of the cells. Then the insulin secretion was expressed as fold change to cells transfected with negative control siRNA. The rat complementary DNAs for Prkar1a and Tagln2 were cloned into pcmv3t3a vector. The cDNAs for each serine (S) or threonine (T) to alanine (A) or aspartic acid (D) mutant were generated by an inverse PCR-based site-directed mutagenesis kit (Toyobo, Cat. No. SMK-101). The cDNAs for Tagln2, Tagln2 S163A and Tagln2 S163D were subsequently subcloned into pmscvpuro vector, respectively. INS-1E cells (originally supplied by Dr. Claes Wollheim, Geneva, Switzerland) were cultured as previously described (10). The generation of stable INS1E cell lines constitutively expressing pmscvpuro-HA, Tagln2, Tagln2 S163A, or Tagln2 S163D was performed with retrovirus made from Plat-E cells (26). For Prkar1a, lipo LTX (Invitrogen, Cat. No. 15338-100) was used to overexpress wild type and respective mutants in INS-1E cells, according to the manufacturer’s recommendations. The phenotypes were analyzed 72 hours after


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transfection as described above. The insulin secreted was firstly normalized by the protein content of the cells. Then the insulin secretion was expressed as fold change to cells overexpressed with empty vector. The data was analyzed with Student’s t test (two-tailed), or Welch’s t test (two-tailed) for unequal sample variance. See supplementary text for details and catalogs of antibodies. Results A SILAC-based workflow for islet quantitative phosphoproteomic analysis Approximately 300 - 400 islets per rat were obtained in preparation. Examination under a stereomicroscope showed good morphological integrity and purity of the isolated islets (Fig. 1A). Functional integrity test demonstrated that isolated islets exhibited strong response to 30 min high glucose treatment and showed significant insulin secretion as well (Fig. 1B). Then we applied our SILAC-based work flow to monitor the islet phosphoproteomic changes in this process, corresponding to the second-phase insulin secretion. As is illustrated in Fig. 1C, briefly, our workflow involved rat islets isolation (n = 11 rats), dividing the islets from a single rat into two equal aliquots (varying from 20 µg to 47 µg), treating the two aliquots with 30 min basal 2.8 mM or stimulatory 16.7 mM glucose, respectively, mixing each of the two aliquots from one rat with equal amount of internal standard, trypsin digestion, phosphopeptides enrichment with TiO2 and LC-MS/MS analysis. Instead of pooling samples, we used islets from an individual as a pair of samples to minimize effects of biological variation and collect more reliable and statistical results. SILAC-based quantitative strategy allowed combination of internal standard and samples at the beginning of sample processing, which could greatly minimize systematic errors. Here we selected rat insulinoma-derived INS-1E beta cells labeled via SILAC (9) as internal standard, considering


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beta cells comprises 60% - 80% of the islet structure in rat (27). It is well-known that arginine could be converted into proline in certain cell lines (11), inducing low labeling efficiency and a bias in quantification, as was observed in INS-1E cells in former research (28) and our preliminary experiment. Here we optimized the regular recipe of RPMI1640 medium by decreasing arginine concentration and increasing proline concentration to suppress arginine-toproline conversion (11). Finally, the labeling efficiency of internal standard cell was 97.5% after optimization (Fig. S1A). Heavy proline-containing peptides were seen less than 1%, and the same distributions of peptides with and without proline showed the arginine-to-proline conversion was effectively controlled (Fig. S1B). We combined in-tip and best-ratio phosphopeptide enrichment strategy to approach the extremely limited islet sample amount (29, 30). TiO2 beads and C8 silica were packed in a tip to form the enrichment and de-salting bilayer thus the enrichment was performed in a pipette tip to minimize sample loss (Fig. 1D). Our previous work showed peptide-to-beads ratio would greatly influence the enrichment results, and for different biological samples the best-ratio would be varied (30). Therefore it is crucial to optimize the peptide-to-beads ratio for specific sample, especially for limited sample amount from islets in single rat. In this work, we tested different ratios of peptide-to-beads, and the best peptide-to-beads ratio of 1:2 was determined at starting stage (Fig. 1E). Finally, this in-tip combining best-ratio strategy was used for high-sensitive phosphopeptide enrichment to achieve the high-efficient sample preparation. Comparative phosphoproteomic analysis of glucose-treated islets from individual rat Eventually, from 22 LC-MS/MS runs of islet phosphopeptides (11 rats, 2.8 mM and 16.7 mM glucose treatment for each rat, 22 samples totally), 8,539 phosphosites derived from 2,487 proteins were identified, in which 6,207 distinct class I sites (Table S1) with high confidence


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were successfully identified (localization probability ≥ 0.75 and score difference ≥ 5) (31). Quantitative data generated on 2,877 phosphosites (with ratios in at least three rats) revealed 170 significantly regulated phosphosites (Table S2) responding to short-term high glucose treatment (Fig. 2A). Summarized from evidence.txt of MaxQuant result, 94.5% phosphopeptides identified in islet were quantified, showing a high overlap between INS-1E cells and islet phosphoproteome. It is suggested that one should aim to have protein ratios < 10-fold in order to achieve accurate quantification in SILAC, and ideally more that 90% of all proteins within a 5-fold ratio (11). In protein quantitation with SILAC, variation may be balanced out by different peptides, while in phosphosite quantification based on single phosphopeptide, the variation will be a little larger (32). In our data the distribution of ratios between heavy and light amino acid-containing phosphopeptides was unimodal (Fig. S1C). Ratios between 0.1-fold and 10-fold accounted for 93.2-97% (average: 94.8%) of all the ratios, and ratios between 0.2-fold and 5-fold accounted for 84.3-89.7% (average: 87%), indicating the accuracy of our data and a good match between rat islet and INS-1E cells. The distribution of “ratio of ratios” between basal and stimulatory glucose-treated islet was also remarkably sharp after normalization, demonstrating high reliability of the data (Fig. S1D). Analysis of precursor mass deviation shows 90% of all phosphopeptides were identified with a mass deviation less than about 2 ppm (Fig. S1E). Overall, these data qualified the SILAC-based and the in-tip and best-ratio phosphopeptide enrichment strategy as a robust workflow for islet quantitative phosphoproteomics. Totally, 87% of the identified islet phosphopeptides were singly phosphorylated and 13% were multi-phosphorylated (Fig. 2B). The islet phosphosites were composed of 89% phospho-Ser sites, 10% phospho-Thr sites, and 1% phospho-Tyr sites (Fig. 2C), largely in accordance with


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previous reports (31). About 85% of the identified phosphosites were annotated as novel and not included in PhosphoSitePlus database (33) in rat, and more than 50% and 67% were annotated in human and mouse orthologs, respectively (Fig. 2D). As the first illustration of rat islet phosphoproteome, our data represented a significant addition to rat phosphoproteome. The identified phosphoproteins were classified by Gene Ontology annotation system for biological pathway and protein class analyses (Fig. 2E). Notably, we found that these islet phosphoproteins were enriched in biological pathways mainly involved in nucleic acid metabolism, cell cycle, protein transport, translation related pathway and DNA binding proteins, protein kinases, and cytoskeletal proteins, indicating that these pathways and kinds of proteins were preferentially targeted by phosphorylation in rat islets. Meanwhile, we also found a paucity of phosphoproteins in biological pathways of cell-cell adhesion and respiratory electron transport chain, and protein classes of protease, oxidoreductase and GPCR, etc. This was consistent with previous reports that mitochondria and extracellular matrix were under-represented in phosphorylation (34). Together, these analyses began to capture the global plasticity of the islet phosphoproteome, and highlighted individual phosphosites that could be vital to islet function in response to glucose. A quantitative map of glucose-responsive phosphodynamics in islet With SILAC-labeled INS-1E cells as internal standard, altogether we revealed 170 phosphosites (6% of the 2,877 quantified phosphosites) in islet responding to 30 min high glucose treatment, of which 98 was up-regulated and 72 was down-regulated (Table S2). According to previous reports, we manually organized most of the modulated phosphosites into pathways, protein complexes or organelles (Fig. 3). Proteins with these phosphodynamics were mainly involved in insulin secretion, cytoskeleton dynamics, protein processing in ER and Golgi, transcription and translation, etc.


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Elevation and oscillations in Ca2+ are essential for vesicles transportation and exocytosis in both of the first-phase and second-phase insulin secretion (35). Here in our data the phosphorylation status of Ca2+-dependent kinase Camk2b (S355, S358, S483), L-type Ca2+ channel activity related protein Rem2 (S296), and Ca2+ transporter protein Atp2b1 (S1155, S1178), Slc24a2 (S334) were diversely regulated by short-term high glucose. Cytoskeleton remodeling induced by glucose participates in GSIS by facilitating vesicles transportation and exocytosis (36, 37), especially in second-phase secretion when granules are recruited and transported from a reserve pool located further away (6). Here many microtubule associated proteins (MAP: Mapt (S374), Map4 (S902, S978), Map1b (S1778, T1781), etc.) and F-actin stability regulators (Cfl1 (S2), Dstn (S3), etc.) showed phosphodynamics, of which several phosphosites have been indicated a role in cytoskeleton dynamics. It is reported that upon phosphorylatoin, many microtubule associated proteins would detach from microtubule, resulting in low stability of microtubule (38). For example, Stmn1 S25 was one of the four phosphosites whose down-regulation activated its microtubule destabilizing activity (39). Also, Cfl1, known as regulators of F-actin non-equilibrium assembly and disassembly, promoted actin filament depolymerization when dephosphorylated at S3 (40). In second-phase secretion, cAMP bas been recognized as metabolic coupling factors that played positive roles (6). We also found a few glucose-responsive phosphosites on proteins related to cAMP signaling (Sphkap (S1298), Adcy9 (S152), etc.). Besides, phosphorylation status of several insulin secretary granules related proteins (Ptprn2 (S681, S687, S693), Rab3d (S10), etc.) were also changed. These changed phosphosites could indicate precise mechanisms between the known corresponding factors (Ca2+, cytoskeletal dyanmics, cAMP, etc.) and insulin secretion.


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In addition to insulin secretory granule exocytosis, the most significant response of glucosestimulated islet, other multi-level networks co-operatively responded to short-term high glucose as well. Many protein processing (Sec22b (S137), Copa (S402), Golga4 (S121), etc.), transcription (Pdcd4 (S457), etc.) and translation (Eif4g1 (S1210), Rps6 (S240), etc.) related proteins showed phosphodynamics in this study. Collectively, these results indicated that multilevel phosphorylation networks coordinately responded to high glucose in second-phase insulin secretion, and suggested a novel subset of highly connected phosphosites that were probable mediators between glucose treatment and various islet responses, especially insulin secretion. Kinase analysis of the islet phosphoproteome With the qualitative results of 6,207 distinct islet phosphosites, we were curious which kinases might be responsible for these modifications in this highly specialized tissue. The relationships between kinases and identified phosphosites were predicted with GPS2.1 (23). We then got 15 S centered and 10 T centered significantly enriched consensus motifs with Motif X (22) (Fig. S2). By matching motifs with kinases, we presumed that 40 kinase groups were more related to the phosphorylation of islet phosphoproteins, corresponding to 25 motifs (Fig. 4A). Substrates with basic motifs (…R..S…., …RS.S…., etc.) were most probably recognized and phosphorylated by Ca2+ and calmodulin-regulated kinase (CAMK family, including CAMK2, CAMKL, MAPKAPK, RAD53) and protein kinase A, G, C families (AGC family, including PKA, RSK, SGK, AKT, etc.). Acidic motifs (......T.D…., ……S.E…., etc.) were predicted to be substrates of casein kinases I and II (CK I and CK II). Proline-directed motifs (……S.P…, ……SP…., etc.) containing substrates were probably phosphorylated by cyclin-dependent kinase (CDK family, including CDC2, CDK4, CDK5), mitogen-activated protein kinase (MAPK family, including p38, JNK, ERK), glycogen synthase kinase 3 (GSK3) and dual specificity tyrosine-


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phosphorylation regulated kinase (DYRK). These kinases were highly correlated to the phosphorylation events in varieties of islet functions. Next with the quantification data we engaged to elucidate which kinases were responsible for the dynamic phosphoproteome of islet induced by 30 min high glucose treatment. By testing whether the mean of ratios of the phosphosites matching each kinase groups was significantly different from zero after stimulation, we found cAMP-dependent protein kinase (PKA), ribosomal s6 kinase (RSK), cyclin-dependent kinase (CDK) family members (CDC2 and CDK5), mitogenactivated protein kinase (MAPK) family members (ERK and p38) and protein kinase B (PKB) were most likely among the activated kinases after 30 min glucose stimulation (Fig. 4B). Our findings were supported by previous results that activity of PKA, ERK1/2, PKB, p70S6K, and p38γ were promoted by short-term high glucose (41-44). Among these kinases, PKA activity was shown to be essential for both the first-phase and second-phase insulin secretion via inhibitor experiment (41). These results showed that our strategy was effective and sensitive to capture the kinases with changed activity in islets. Prkar1a pT75pS77 and Tagln2 pS163 were correlated with GSIS The combination analyses of our quantitative phosphoproteomic data enabled us to investigate which phosphorylation events might functionally regulate GSIS, the key process of islet beta cell function. Among the differentially regulated sites special attention was paid to the phosphorylation of Prkar1a T75, S77 and Tagln2 S163 (Fig. 5A, Fig. S3A-C). Prkar1a is the type I-alpha inhibitory regulation subunit of PKA, and has been indicated a role in mouse GSIS (45). Here the phosphorylation levels of Prkar1a T75 and S77 were significantly up-regulated about 1.91-fold and 2.16-fold (Fig. 5A). Up-regulation of Prkar1a pS77 was verified by western blot (Fig. 5B). Then we compared the GSIS phenotypes among INS-1E cells


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transiently overexpressed with either Prkar1a, Prkar1a T75A, Prkar1a S77A, Prkar1a T75AS77A or empty vector as control. Over-expression of wild-type rat Prkar1a promoted GSIS significantly. Double dead mutant T75AS77A with equivalent overexpression significantly lowered the GSIS-promotion effect compared with wild-type protein (Fig. 5C, Fig. S3D). The combination of Prkar1a T75 and S77 phosphorylation induced by glucose most likely played a positive role in GSIS. Besides, the insulin content was not influenced by overexpression of Prkar1a, Prkar1a T75A, Prkar1a S77A or Prkar1a T75AS77A (Fig. S4A). The phosphorylation of Prkar1a T75 and S77 might contribute to GSIS via other mechanisms rather than influencing insulin biosynthesis. The phosphorylation of Tagln2 S163 and S83 by PFTK1 were previously reported to attenuate its binding ability to F-actin, resulting in high dynamics of F-actin (46). Considering the significance of cytoskeleton in insulin secretion (36, 47), we were curious if phosphorylated Tagln2 played a role in GSIS. Here we did not identify the phosphorylation of Tagln2 S83, while the phosphorylation of Tagln2 S163 was significantly down-regulated about 0.47-fold (Fig. 5A). Firstly we validated the SILAC data with synthetic heavily labeled peptide and selected reaction monitoring (SRM) (Fig. 5D, Fig. S3E). As Fig. 5D showed, 30 min high glucose treatment indeed significantly decreased the phosphorylation of Tagln2 S163 and this down-regulation could be observed as early as 5 min (Fig. S3F). Down-regulation of tagln2 expression using siRNA caused decreased insulin secretion upon high glucose treatment (Fig. 5E, Fig. S3G). Next, we compared the GSIS phenotypes in INS-1E cells with stable over-expression of either Tagln2, Tagln2 S163A, Tagln2 S163D or empty vector as control. Interestingly, at high glucose level, wild-type Tagln2 promoted insulin secretion, while mutant S163A and S163D with parallel overexpression both had lower GSIS-promotion effect than wild-type protein (Fig. 5F,


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Fig. S3H). Moreover, neither overexpression of Tagln2, Tagln2 S163A and Tagln2 163D or knockdown of tagln2 showed distinct effect on insulin content (Fig. S4B, Fig. S4C). Based on the available evidence, it could be confirmed that the phosphorylation of Tagln2 S163 was involved in GSIS without significantly altering insulin biosynthesis. Discussion In the current study, we firstly developed a SILAC-based strategy to quantitatively describe the phosphoproteome of islets from individual rat and provided to our knowledge the first systemwide islet phosphoproteome, especially the reliable subset of phosphosites regulated in GSIS. In SILAC labeling, heavy internal standard and samples are mixed at the first step of sample preparation, maximally eliminating systematic errors brought by separate trypsin digestion, etc. This should not be ignored when approaching low amount of materials, which could suffer a lot from these technical variations. In view of the inter-individual variability of rat physiology, the responses to glucose stimulation might be diverse among individuals. Stimulation and analysis of phosphoproteome in each individual could provide more reliable and statistical results (Table S2). Therefore here we divided the islets from single rat into two equal aliquots as a pair of samples for basal and stimulatory glucose treatment to avoid overlooking the inter-individual variability. However, this brought more challenges in islet isolation, sample handling and phosphopeptide enrichment, since the islet protein amount divided equally from single rat were extremely low. Generally, as the low stoichiometry nature of phosphopeptides, abundant sample amount (hundreds of micrograms to several milligrams) is required for most in-depth phosphoproteomic

analyses

(48).

Few

studies

have

approached

low

amount

of

phosphoproteomic samples (20 µg to 47 µg in our study). Here, we optimized the in-tip and bestratio phosphopeptide enrichment strategy for islet lysates to achieve high enrichment


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performance and minimize sample loss and contamination. Finally, via SILAC-based and in-tip and best-ratio phosphopeptides enrichment strategy, our ideal platform for limited islet sample successfully identified 6,207 high reliable islet phosphosites. Here we designated phosphosites with high/low glucose ratios (from 2 dependent RAW files) in at least 3 rats (from 6 RAW files totally) as quantified. Owing to the partial stochastic nature of shotgun proteomics, the overlap between runs may be low. From 11 rats, we eventually quantified 2,877 phosphosites with high accuracy. Quantitative islet phosphoproteome analysis revealed 170 diversely regulated phosphosites responding to short-term high glucose. In our analyses, 30 min high glucose stimulation corresponded to the second-phase insulin secretion. Although insulin secretion is often observed and designated as first-phase and second-phase secretion, the triggering pathways and amplifying pathways mainly involved in first-phase and second-phase secretion show more similarities than differences. Both pathways interact to achieve temporal control and amplitude modulation of biphasic insulin secretion (35). Although we observed phosphoproteome remodeling on several biological levels that were related to second-phase secretion previously, it is hard to conclude that the glucose-responsive phosphosites dataset here is related to second-phase secretion exclusively. Nonetheless, here the specific glucose-responsive subset of phosphosites still provides valuable clues. For instance, accumulating evidence suggests that cAMP, Ca2+ and cytoskeletal dynamics participate in GSIS, but the precise mechanisms and components are still lacking. Our data revealed lots of specific phosphosites that were potential mediators for the process. And what’s more, functional validation also confirmed the correlation between several phosphosites from the dataset and insulin secretion. In addition to inducing apparent insulin secretory granules exocytosis, it seemed glucose triggered several other responses as well. For


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example, there was some phosphorylation remodeling occurring on protein processing in ER and Golgi, transcriptional and translational machines in second-phase GSIS. It was speculated that this could be a trigger that altered the activity of transcription and translation for certain proteins, which ultimately contributed to maintain appropriate stores of insulin secretory granules in beta cells. Since former reports have shown that (prepro)insulin was induced at transcriptional level upon short-term high glucose treatment, elevation of proinsulin and proinsulin endopeptidases PC2/PC3 at protein levels was also previously observed (49-51). Here several kinases were predicted to be activated with short-term high glucose stimulation. With inhibitor experiment in a mouse beta cell line MIN6, results showed that ERK1/2 activity contributed to the short-term (5 min - 15 min) 10 mM glucose-stimulated insulin secretion, but not to the long-term (more than 30 min) 10 mM glucose-stimulated insulin secretion (52). Besides, inhibitor experiment also showed that ERK1/2 is involved in glucose-stimulated insulin gene expression (53). For PKB, CDK5 and p38, it is still a controversial issue whether these three kinases play a positive or negative role in GSIS based on former evidence (54-58), which may be brought by different treatment time courses or different experimental systems. RSK and CDC2 were less investigated in insulin secretion before. Also, as is mentioned above, this shortterm high glucose stimulation also triggered phosphorylation status alteration in transcription, translation, protein processing, etc. There is possibility that these kinases might not be directly involved in insulin secretion process, but play a role in these processes. To obtain some insight into whether any of the distinctly modulated phosphosites would influence GSIS, we investigated the functional roles of several phosphosites in INS-1E cells. The phosphorylations of Prkar1a T75 and S77 were confirmed to be related to GSIS here. We presumed that phosphorylation of S77 might have more contribution to GSIS than T75, for T75A


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mutant promoted GSIS almost the same as wild-type form, while S77A mutant lost most of its function in increasing GSIS. It is also possible that these two sites had some redundancy in regulating GSIS, since double mutant exhibited a severer impairment than single mutant. It still remained elusive whether Prkar1a impacted GSIS in PKA-dependent or PKA-independent ways, or both, since Prkar1a also has other biological functions independent of PKA catalytic subunits (59), and Prkar1a might be involved in the regulation of GSIS through different mechanisms at diverse levels. In a previous study, the researchers conditionally ablated prkar1a in pancreas through inbreeding floxed prkar1a and pancreas-specific Pdx1-Cre mice. The conditional ablation mice displayed improved GSIS (45). It was probable that different mechanisms were involved in altered GSIS in rat Prkar1a over-expressed INS-1E beta cells and mice with ablation of prkar1a in pancreas. Besides, former studies showed that Prkar1a phosphorylation at S83 by CDK2/cyclin E was necessary for its interaction with replication factor C complex (RFC40) and subsequently translocation of RFC40 to the nuclear (59). However, in our study, no obvious changes were observed for the phosphorylation of Prkar1a S83 (fold change = 0.99, n = 11 (rats)) after high glucose stimulation. All these evidence indicate that Prkar1a is differentially and complicatedly modulated in diverse biological processes. Through SILAC data and SRM validation, we proved that phosphorylation of Tagln2 S163 was bona fide down-regulated after glucose stimulus. However, promotion of GSIS was induced by over-expression of Tagln2, which was dependent of its phosphorylation of S163. Meanwhile, inhibition of GSIS was observed in tagln2 knockdown INS-1E cells. The Tagln2 S163D probably failed to mimic the constitutive phosphorylation state of Tagln2 at S163, since it had the same phenotype with Tagln2 S163A. It could be that the dephosphorylation of Tagln2 S163 was associated with negative feedback regulation of insulin secretion. Former evidence


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suggested that negative feedback regulation might exist in GSIS to maintain proper insulin secretion, probably partially through enhanced activity of voltage-gated potassium channel by glucose metabolism (60, 61), or by negative modulators (enzymes and pathways that interfere with the production of metabolic coupling factors) and signal removers (enzymes and pathways that degrade metabolic coupling factors) (6). In current work we performed the experiments on rat islets. It is hard to obtain human islets with good qualities and in sufficient numbers due to practical and sometimes ethical difficulties. Although rat islet is not a blueprint for human islet, there are sufficient similarities with high value, to justify more efforts to understand the glucose-stimulated insulin secretion process in rat islet. Globally, characteristics of GSIS in human islets resembled those observed in rodent islets (62). GSIS in both rodent and human islets depended on the metabolism of glucose (63-65), Ca2+ elevation (62), K+ATP channels (62), etc. Some K+ATP-independent amplifying pathways are also functional in both rodent and human islets (e.g. an autocrine and paracrine positive feedback loop involving GABA and GABAA receptor) (6, 62, 66). Also, glucose potentiated insulin secretion induced by arginine or a mixture of amino acids in both rodent and human islets (62). However, it should also be noted that certain differences existed between human islets and rat islets. Human islets contained fewer β cells (~50%) and more α cells (~40%) compared to rat islets (60%~80% β cells, 15%~20% α cells). In human islets, α cells, β cells and δ cells distributed throughout the islet. In rat islets, β cells formed the core of the islet and there was a mantle of other endocrine cells (27, 67). Functionally, human and rodent islet β cells used different glucose transporter (Glut2 in rodent, Glut1 and Glut3 in human) (68). Human islets preferentially depended less on pyruvate carboxylase and ATP citrate lyase than rodent islets for GSIS (69). Threshold glucose concentration in human islet was lower than rodent islet (62). The


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differences between rat and human islet should be considered when making further efforts in investigating mechanisms behind these regulated phosphosites. On the other side, structurally human islet size is comparable to rodent islet (70). With optimization of isolation procedure (enzyme concentration, density gradient, etc.), human islet could be separated with good morphological and functional integrity as rodent islet (71, 72). Our workflow could be well applicable to analyze human islets, through which researchers could gather information more pertinent to human. Conclusions Taken together, with our robust SILAC-based workflow, here we elucidated important phosphorylation events and kinases that are regulated in islet GSIS. We validated that three phosphosites from the dataset were indeed associated with insulin secretion, suggesting its reliability and prospect for application in further dissecting signaling pathways involved in islet physiology. While our work provided an in-depth assessment of islet phosphorylation, further functional investigations will be required to pinpoint the mechanims by which these kinases and phosphosites may regulate islet function, especially GSIS. Our highly effective and sensitive workflow is also applicable to analyze dynamic islet phosphoproteome stimulated with different drugs or analyze islets of diabetic subjects. This could open a door for molecular researchers in diabetes field to exploit the high-throughput and robust islet quantitative phosphoproteomics, which could greatly advance our understanding on how drugs work or mechanisms of islet dysfunction in diabetes.


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FIGURES

Figure 1. A SILAC-based workflow for islet quantitative phosphoproteomic analysis. (A) Purity of the islets was evaluated under a microscope before (left) and after (right) dithizone staining (scale bar: 50 µm). (B) The function of the isolated islets was evaluated with 30 min GSIS (***: P ≤ 0.001, n = 5, error bar: SEM). (C) Flowchart of phosphoproteome analyses on rat islets. (D) Workflow of in-tip phosphopeptide enrichment procedure. (E) Best-ratio determined by test of different peptide-to-beads ratios.


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Figure 2. General properties of the rat islet phosphoproteome data. (A) Summary of the phosphorylation data. (B) Distribution of phosphorylation numbers in the datasets of all identified class I sites. (C) Distribution of phosphorylated serine, threonine and tyrosine in the datasets of all identified class I sites. (D) Comparison of all identified phosphosites with PhosphoSitePlus database. About 67%, 50%, 15% and 1% of the phosphosites can be matched to mouse, human, rat and others (dog, pig cow, chicken rabbit, hamster, cat and quail) in the PhosphoSitePlus database. (E) Identified phosphoproteins were assigned to GO terms for biological pathway and protein class using PANTHER. “+” stands for overrepresentation and “-” for downrepresentation of the identified phosphosites compared to the set of all rat genes using the bonferroni correction for multiple testing (P < 10-4).


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Figure 3. Most of the changed phosphosites were graphically representated. Phosphosites in yellow background were up-regulated and blue background were down-regulated in rat islets with 30 min high glucose treatment.


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Figure 4. Kinase analysis of rat islet phosphoproteome. (A) Kinase analysis of islet phosphoproteome. Kinases predicted to phosphorylate less than 10% of the sites matching the motifs were discarded. Fractions of motifs matched to the kinase (%) were illustrated in the heatmap. (B) Predicted kinases with changed activity after high glucose treatment in rat islet. Relative kinase activity was expressed as average fold change (log2-transformed) of the phosphosite matching the kinase. Relative activity of the seven kinases was significantly different from zero on the confidence level of 0.005 (two-tailed).


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Figure 5. Prkar1a pT75pS77 and Tagln2 pS163 were correlated with GSIS. (A) Fold changes of Prkar1a pT75 and pS77, and Tagln2 pS163 upon 30 min high glucose treatment in SILAC data (*: P < 0.05, **: P < 0.01, ***: P < 0.001, n = 6 for Prkar1a pS77, n = 10 for Prkar1a pT75, n = 9 for Tagln2 pS163). (B) Validation of the Prkar1a pS77 up-regulation upon 30 min high glucose treatment by western blot. (C) INS-1E cells were overexpressed with control vector pcmv3t3aFLAG, wild type Prkar1a, Prkar1a T75A, Prkar1a S77A and Prkar1a T75AS77A and subjected to 1 h GSIS experiment. (*: P < 0.05, **: P < 0.01, n = 9, error bar: SEM). (D) Validation of the down-regulated change of Tagln2 pS163 upon 30 min high glucose treatment by SRM (***: P < 0.001, n = 3, error bar: SEM). The best five transitions were finally used for SRM quantitation (Fig. S3F). (E) tagln2 knockdown decreased GSIS in INS-1E cells (***: P < 0.001, n = 6, error bar: SEM). (F) INS-1E cells were stably overexpressed with control vector pmscvpuro-HA, wild type Tagln2, Tagln2 S163A and Tagln2 S163D. One hour GSIS of the stable cell lines was tested (*: P < 0.05, **: P < 0.01, n = 9, error bar: SEM).


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ASSOCIATED CONTENT Supporting information Supplementary text: supplementary materials and methods Figure S1. Quality control of the data. Figure S2. Phosphorylation motifs analysis. Figure S3. MS/MS spectra for the three selected phosphosites, SRM transition for Tagln2 pS163 validation, siRNA efficiency and overexpression efficiency in knockdown and overexpression experiments. Figure S4. Insulin content was not significantly influenced by phosphorylation of Prkar1a T75S77 or Tagln2 S163. Table S1. 6207 identified class I phosphorylation sites of rat islet. Table S2. 2877 quantified and 170 regulated phosphorylation sites in islet GSIS. AUTHOR INFORMATION Corresponding Author *

To whom correspondence should be addressed: Rong Zeng, [email protected], Tel: 86-21-

54920160; Jiarui Wu, [email protected], Tel: 86-21-54921128. Author Contributions J.L. assisted with design of the studies, performed the experiments, data analysis and wrote the manuscript. Q.L. contributed to the optimization of phosphopeptides enrichment workflow, ran


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the mass spectrometry analysis and reviewed the manuscript, J.T. optimized the culture system for SILAC-labeling INS-1E, J.L. and F.X. performed the SRM experiment, J.W. reviewed the manuscript, R.Z assisted with the design of the studies, data interpretation, and reviewed the †

manuscript. All authors have given approval to the final version of the manuscript. These authors contributed equally to this work. Funding Sources This work was supported by the grants from Ministry of Science and Technology of the People's Republic of China (2011CB910200, 2014CB910500) and a grant from the National Natural Science Foundation of China (31130034). Note The authors declare no competing financial interest. ACKNOWLEDGMENT We thank Shuai Han for technical assistance.


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REFERENCES 1. Kolb, H.; Eizirik, D. L., Resistance to type 2 diabetes mellitus: a matter of hormesis? Nat Rev Endocrinol 2011. 2. Zhou, J. Y.; Dann, G. P.; Liew, C. W.; Smith, R. D.; Kulkarni, R. N.; Qian, W. J., Unraveling pancreatic islet biology by quantitative proteomics. Expert Rev Proteomics 2011, 8, (4), 495-504. 3. Waanders, L. F.; Chwalek, K.; Monetti, M.; Kumar, C.; Lammert, E.; Mann, M., Quantitative proteomic analysis of single pancreatic islets. Proc Natl Acad Sci U S A 2009, 106, (45), 18902-7. 4. Nesher, R.; Anteby, E.; Yedovizky, M.; Warwar, N.; Kaiser, N.; Cerasi, E., Beta-cell protein kinases and the dynamics of the insulin response to glucose. Diabetes 2002, 51 Suppl 1, S68-73. 5. Chen, X. Y.; Gu, X. T.; Saiyin, H.; Wan, B.; Zhang, Y. J.; Li, J.; Wang, Y. L.; Gao, R.; Wang, Y. F.; Dong, W. P.; Najjar, S. M.; Zhang, C. Y.; Ding, H. F.; Liu, J. O.; Yu, L., Brainselective kinase 2 (BRSK2) phosphorylation on PCTAIRE1 negatively regulates glucosestimulated insulin secretion in pancreatic beta-cells. J Biol Chem 2012, 287, (36), 30368-75. 6. Prentki, M.; Matschinsky, F. M.; Madiraju, S. R., Metabolic signaling in fuel-induced insulin secretion. Cell Metab 2013, 18, (2), 162-85. 7. Han, D.; Moon, S.; Kim, Y.; Ho, W. K.; Kim, K.; Kang, Y.; Jun, H., Comprehensive phosphoproteome analysis of INS-1 pancreatic beta-cells using various digestion strategies coupled with liquid chromatography-tandem mass spectrometry. J Proteome Res 2012, 11, (4), 2206-23. 8. Cui, Z.; Hou, J.; Chen, X.; Li, J.; Xie, Z.; Xue, P.; Cai, T.; Wu, P.; Xu, T.; Yang, F., The profile of mitochondrial proteins and their phosphorylation signaling network in INS-1 beta cells. J Proteome Res 2010, 9, (6), 2898-908. 9. Monetti, M.; Nagaraj, N.; Sharma, K.; Mann, M., Large-scale phosphosite quantification in tissues by a spike-in SILAC method. Nat Methods 2011, 8, (8), 655-8. 10. Merglen, A.; Theander, S.; Rubi, B.; Chaffard, G.; Wollheim, C. B.; Maechler, P., Glucose sensitivity and metabolism-secretion coupling studied during two-year continuous culture in INS-1E insulinoma cells. Endocrinology 2004, 145, (2), 667-78. 11. Geiger, T.; Wisniewski, J. R.; Cox, J.; Zanivan, S.; Kruger, M.; Ishihama, Y.; Mann, M., Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat Protoc 2011, 6, (2), 147-57. 12. Lacy, P. E.; Kostianovsky, M., Method for the isolation of intact islets of Langerhans from the rat pancreas. Diabetes 1967, 16, (1), 35-9. 13. Nielsen, P. A.; Olsen, J. V.; Podtelejnikov, A. V.; Andersen, J. R.; Mann, M.; Wisniewski, J. R., Proteomic mapping of brain plasma membrane proteins. Mol Cell Proteomics 2005, 4, (4), 402-8. 14. Wisniewski, J. R.; Zougman, A.; Nagaraj, N.; Mann, M., Universal sample preparation method for proteome analysis. Nat Methods 2009, 6, (5), 359-62. 15. Olsen, J. V.; Vermeulen, M.; Santamaria, A.; Kumar, C.; Miller, M. L.; Jensen, L. J.; Gnad, F.; Cox, J.; Jensen, T. S.; Nigg, E. A.; Brunak, S.; Mann, M., Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci Signal 2010, 3, (104), ra3.


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Quantitative Phosphoproteomics Revealed Glucose-Stimulated

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