ARTICLE

Received 11 May 2016 | Accepted 15 Sep 2016 | Published 14 Nov 2016 DOI: 10.1038/ncomms13250 OPEN Glucose-regulated and drug-perturbed phosphoproteome reveals molecular mechanisms controlling secretion

Francesca Sacco1, Sean J. Humphrey1,Ju¨rgen Cox1, Marcel Mischnik2, Anke Schulte3, Thomas Klabunde2, Matthias Scha¨fer3 & Matthias Mann1

Insulin-secreting beta cells play an essential role in maintaining physiological blood glucose levels, and their dysfunction leads to the development of diabetes. To elucidate the signalling events regulating insulin secretion, we applied a recently developed phosphoproteomics workflow. We quantified the time-resolved phosphoproteome of murine pancreatic cells following their exposure to glucose and in combination with small molecule compounds that promote insulin secretion. The quantitative phosphoproteome of 30,000 sites clustered into three main groups in concordance with the modulation of the three key : PKA, PKC and CK2A. A high-resolution time course revealed key novel regulatory sites, revealing the importance of methyltransferase DNMT3A in the glucose response. Remarkably a significant proportion of these novel regulatory sites is significantly down- regulated in diabetic islets. Control of insulin secretion is embedded in an unexpectedly broad and complex range of cellular functions, which are perturbed by drugs in multiple ways.

1 Department of Proteomics and , Max Planck Institute of Biochemistry, Martinsried 82152, Germany. 2 Sanofi Aventis Deutschland GmbH, R&D, LGCR, SDI, Bioinformatics, Frankfurt 65926, Germany. 3 Sanofi Aventis Deutschland GmbH, Global Diabetes Division, R&TM, Islet Biology, Frankfurt 65926, Germany. Correspondence and requests for materials should be addressed to M.M. (email: [email protected]).

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iabetes is a complex and heterogeneous condition modulated (analysis of variance (ANOVA), false accompanied by a deterioration of glycaemic control. discovery rate (FDR)o0.05), and investigated whether these DChronic hyperglycaemia can lead to a myriad of proteins were enriched for particular biological processes or complications, including coronary artery disease and myocardial pathways (Fig. 1c). Remarkably we found that proteins involved infarction, stroke, limb amputation, blindness and failure1. in vesicle trafficking and secretion were downregulated in pre- Beta cell dysfunctional patterns such as delayed insulin diabetic islets. This observation may shed further light at the secretory rate to a stimulus or loss of pulsatile secretion of molecular level on the decreased insulin secretion capability insulin represent the major contributor to the initiation and observed during pre-diabetes13. Our data reveal that decreased genetic susceptibility to diabetes2–4. Therefore identifying new insulin secretion is accompanied by a concomitant reduction in molecular mechanisms contributing to insulin secretion could proteins associated with glucose metabolism, while many proteins improve the understanding, treatment and prevention of diabetes. involved in the regulation of fatty acids and proteins metabolism Growing evidence suggests that post-translational are significantly upregulated (Fig. 1c). To our knowledge this is modifications, especially phosphorylation, play a crucial role in the first proteomic data set describing proteome-wide controlling glucose-mediated insulin secretion5,6. In the beta cells remodelling occurring in mice at different stages of type 1 of healthy islet, glucose induces insulin secretion rapidly, within a diabetes (Supplementary Data 1). few minutes and maintained for about an hour. This process We next investigated proteome changes occurring in the NOD therefore likely relies heavily on the modulation of protein islets that may impact the wiring of signalling networks. Of activities through signal transduction pathways, rather than particular relevance was the finding that the levels of key kinases, controlling expression. Additionally, by applying mass including AKT2, RAF1, PKA and CaMK2, as well as several spectrometry (MS)-based proteomics, here we demonstrate that phosphatases were significantly modulated in diabetic islets key kinases are significantly downregulated in NOD (non-obese (Fig. 1d). This highlights the importance of phosphorylation diabetic mice) diabetic islets, further supporting the importance signalling networks in controlling the GSIS. of phosphorylation-based signalling networks in the glucose- stimulated insulin secretion (GSIS). However, few studies have investigated changes to the global Validation of the experimental model. To further characterize phosphoproteome occurring after glucose stimulation in beta the signalling pathways involved in GSIS, we employed Min6 cells, and their depth (number of quantified sites), was insufficient cells, a widely-used insulinoma beta cell line of murine origin to capture many of the important regulatory sites7,8. that is capable of eliciting a robust insulin secretion response Our group recently described a MS-based phosphoproteomics following acute stimulation with glucose14,15.Althoughthere workflow, termed ‘EasyPhos’, which now enables streamlined and are several alternatives with regards to cell lines for studying very large-scale phosphoproteome analysis from limited starting beta cell function, there is an ongoing discussion as to how material over multiple experimental conditions9. Here we apply faithfully they represent the biological process of interest. We EasyPhos to comprehensively quantify the response of the reasoned that similar expression values of proteins involved in a phosphoproteome of murine insulin-secreting beta cells exposed process between two systems would suggest that these processes to glucose, and in combination with seven different compounds are well preserved16. To quantitatively address this, we known to act on different pathways affecting insulin secretion. An performed deep proteomic profiling of both Min6 cells and in-depth proteomic characterization of our chosen cell line model murine pancreatic islets, which is their in vivo cellular context, against pancreatic islets validates the experimental system. We and of which they constitute 80% of cellar mass17.Asinour subsequently combined our deep compound-dependent phos- previous experiments, we employed the recently developed iST phoproteomes with time-resolved phosphoproteomic profiling of proteomics workflow10, combined with label-free LC–MS/MS beta cells stimulated with glucose, revealing phosphorylation sites analysis processed in the MaxQuant environment11,12.This implicated in insulin secretion control and strategy enabled the quantification of about 8,500 proteins in regulation. Remarkably a significant proportion of these novel Min6 cells, and about 7,800 proteins in whole pancreatic islets regulatory sites are significantly downregulated in diabetic islets. (Fig. 2a; Supplementary Fig.2B),makingthisthemost We discover an unexpected connection to epigenetic control comprehensive catalogue of islet-cell proteins to our through the DNA methyltransferase DNMT3A which we knowledge. Importantly, key physiological processes, such as functionally follow up by interaction proteomics. insulin secretion, glycolysis and oxidative phosphorylation, are amply and equally represented in both the proteome of pancreatic islets and Min6 cells (Fig. 2b; Supplementary Results Fig. 2B). Employing the recently developed ‘proteomic ruler’ MS-based proteomics characterization of NOD diabetic islets. method18 we estimated protein copy numbers across the To elucidate the molecular mechanisms governing glucose- complete islet proteomes, revealing consistent rank orders stimulated insulin secretion, we performed deep proteomic between the in vivo and in vitro systems (R ¼ 0.98), as well as profiling of murine pancreatic islets extracted from NOD healthy, excellent agreement between the highest and lowest abundant pre-diabetic and diabetic mice (Supplementary Fig. 1A). We proteins in both cell lines and islets (Fig. 2c). In islets, we were applied the recently developed iST proteomics workflow10, able to measure copy numbers of all the widely used protein combined with label-free LC–MS/MS analysis (Fig. 1a), and markers of beta cells, as well of alpha, PP, delta and epsilon cells. processed the results in the MaxQuant environment11,12. Reassuringly, beta cells markers, such as Ins, Pdx1, Iapp and All experiments were performed in at least biological tripli- Mafa1, are largely equally expressed in Min6 cells and in islets cates, revealing high quantitation accuracy and reproducibility, (Fig. 2d). Conversely, hormones secreted by non-beta cells, such with Pearson correlation coefficients between 0.68 and 0.93 as glucagon, pancreatic polypeptide, somatostatin and ghrelin, (Supplementary Fig. 1B). Remarkably our proteomic data enable are substantially more abundant in islets than in Min6 cells. the unsupervised classification of pancreatic islets according to Apart from validating the Min6 modelatthelevelofexpressed their diabetic stratification (Fig. 1b; Supplementary Fig. 1C). To proteins, these results represent a large resource of protein decipher how the proteome was remodelled in NOD murine islets abundance data for murine beta cells and pancreatic islets for at different diabetic stages, we focused on the 759 significantly use by the community (Supplementary Data 2).

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ab Not obese diabetic (NOD) mice 40 Pre-diabetic2 Pre-diabetic3 Pre-diabetic1

20

Healthy Pre-diabetic Diabetic Blood glucose 0 100 mg dl–1 150 mg dl–1 400 mg dl–1 Diabetic1 Diabetic2 3–4 biological replicates per condition Healthy1 Component 2 (14.8%) –20 Healthy2 Healthy4 Healthy3 Diabetic3 Islets isolation iST method LC-MS/MS analysis –40 –30 –20 –10 0 10 20 30 40 50 60 70 80 90 Component 1 (46.5%)

cd GO BPs and Kegg pathways enriched Healthy Pre-diabetic Diabetic Pre-diabetic increased FDR<0.05 AkAkt2t2 27.7 28.3 28.2 28.1 26.8 26.9 27.1 26.2 27.8 27.5 3 ArafAraf 27.9 28.4 28.8 28.6 28.1 27.5 26.1 27.4 27.1 27.4 Protein and fat digestion/absorption 0 MMap2k2ap2k2 31.4 31.5 31.5 31.8 30.4 30.8 31 30.9 30.9 31.5 Fat metabolism Mapk1Mapk1 32.3 32.4 32.8 32.4 31.9 31.6 31.8 31.9 31.9 32.6 –3

AKT-MAPK MapkapkMapkapk22 28.3 29.1 28.3 29.3 26.1 26.8 26 27.1 27.2 28.7 RRps6ka4ps6ka4 29.4 29 28.9 29.3 29.7 29.7 29.9 27.8 27.7 29.3 Diabetic decreased 3 Camk2bCamk2b 31.7 32 32.3 32.1 31.6 30.7 31.2 28.5 29.2 31.3 Transmembrane transporter activity Csnk1a1Csnk1a1 28.9 29 29.4 29.7 27.5 27.4 28.4 27 27.6 28.6 0 Cytoplasmic vesicle Csnk2a1Csnk2a1 31.8 31.5 31.2 31.6 32.3 32.4 32.4 30.8 31.2 31.9 –3 Stored secretory granule PrkacaPrkaca 27.8 27.2 28.7 28.3 29.1 27.9 28.3 26.2 27.3 26

kinases Prkag1Prkag1 29.7 29.2 28.6 28.8 29.2 29.2 29.3 27.6 28.5 28.6

Ca2+/cAMP Pre-diabetic decreased SykSyk 30.4 30.6 29.3 30.4 31.6 31.4 31.6 31.7 31.1 30.8 Z-score LFQ intensity

3 PppPpp1r1a1r1a 31.1 30.6 31.5 30.9 29.4 29.7 30.6 27.1 27 26.8 0 Glucose metabolism PPpp2r2app2r2a 29.7 30.3 30.3 30.4 31.1 31.3 31.6 30.5 30 30.5 Transport vesicle PP2A PPpp2r5epp2r5e 28.7 28.5 28.7 28.5 28.4 27.3 27.9 26.2 26.9 26.9 –3 Ptprn2Ptprn2 31.8 31.8 32.7 32 30.9 31.1 31.3 27.8 30 30.5 PtPtpn18pn18 26 26.4 25.4 27.6 27.4 27.1 28.1 28.4 27.9 27.8

Phosphatases Healthy Pre- Diabetic PTPs PtprnPtprn 31.6 30.9 32 31.5 30.7 30.9 30.6 27.2 29.1 29.8 Min Max diabetic FDR<0.05 FDR<0.05 LFQ Log2 intensity Figure 1 | MS-based proteomic analysis of NOD islets. (a) Schematic representation of the experimental strategy applied to analyse the proteome of pancreatic islets derived from healthy, pre-diabetic and diabetic NOD mice. (b) Principal component analysis (PCA) of NOD islets discriminates the healthy islets from pre-diabetic and diabetic ones. (c) GO-Biological processes and pathways significantly enriched in three representative clusters are shown. (d) Heat map of and phosphatase protein levels in NOD islets.

Phosphoproteomics of beta cell glucose and drug stimulation. phosphoproteomic workflow that we recently developed9. This To characterize signalling pathways involved in GSIS we next resulted in the quantification of more than 35,000 phosphosites quantified the dynamic response of the phosphoproteome in located on 5,698 proteins (65% of the total detected proteome). Min6 cells, by activating or inhibiting the relevant signalling To our knowledge, with the exception of one very large-scale networks in the presence of high or low glucose concentration. To study of mitotically arrested and EGF-stimulated HeLa cells20, this end, we stimulated the cells by a combination of low or high this is the largest quantified phosphoproteome to date. This is glucose concentration for 10 min, or high glucose concentration made even more remarkable by the fact that previous deep for 30 min. This was combined with one of seven compounds phosphoproteomes have typically relied on extensive fractio- promoting insulin secretion: Glibenclamide, 8-bromo-cAMP, nation, while the experiments performed here were performed 8-bromo-cGMP, ATP, Carbachol, GLP-1 and the GSK3 inhibitor exclusively in single-run mode. Intensity-ranked signal intensities SB216763, resulting in a total of 25 different signalling states of the 35,058 quantified phosphosites span a wide dynamic range, (Fig. 3a). We selected these compounds on the basis of the and phosphorylation sites on very low abundant proteins were involvement of their targets in different parts of insulin secretion also detected (Supplementary Fig. 3A). In total 81% of the pathways (Fig. 3b). All experiments were performed in at least phosphoproteome (28,637 of 35,058 sites) was localized with biological triplicate. To evaluate the efficacy of each of these single amino acid resolution (median localization probability conditions, we directly measured insulin secretion after each 0.999, Supplementary Data 3). In nearly all experimental treatment. All treatments robustly increased insulin secretion, conditions we quantified 413,000 phosphosites (Supplementary and consistent with published reports19, secreted insulin reached Fig. 3B,C), with a high degree of overlap between them a peak at 30 min of glucose stimulation (Fig. 3c). (Supplementary Fig. 3D). Biological replicates measured for To quantify changes to the global phosphoproteome over each condition demonstrated high quantitation accuracy and these 25 different experimental conditions in great depth and reproducibility with Pearson correlation coefficients between with minimal measurement time, we employed the EasyPhos 0.75 and 0.90 (Supplementary Fig. 3E,F). Comparison of

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a b Murine Pancreatic secretion Islets pancreatic islets Insulin secretion Min6 Glycolysis 9,063 quantified proteins Glucose import 374 islets specific proteins TCA iST method LC–MS/MS analysis Oxidative phosphorylation 7,465 mTOR signalling pathway MAPK signalling pathway 1,196 Min6 specific Insulin signalling pathway proteins Phosphatase activity Kinase activity Murine beta cell line (Min6) Transcription factor 0 20406080100 Coverage (%) c Islets Min6 Islets d 7.6 Tmsb10 Zcchc10 Tmem50b Tmsb10 Min6 1.80E+07 7.4 Tmsb4x Tmsb4x

Beta cells PP cells 7.2 Syngr2 Atp5e 1.60E+07 Tmsb15b1 Lsm5 Delta cells Islets 7 Atp5e Rps29 1.40E+07 Tomm5 Usmg5 Min6 6.8 Rps29 Tomm7 Epsilon cells 1.20E+07 6.6 Usmg5 Tomm5

6.4 1.00E+07 Alpha cells 6.2 8.00E+06 6 Islets Min6 6.00E+06 5.8 Ttn Ttn Copy number per cell 4.00E+06 5.6 Syne1 Ubr4 Macf1 5.4 Ush2A Syne2 Syne1 2.00E+06 5.2 Eppk1 Macf1 Log10 copy number per cell Mll2 Col12a1 0.00E+00 5 Kiaa1109 Fbn1 Hmcn1

Eppk1 Sst Ppy Gck

4.8 Gcg Ghrl Ins2 Ins1 Rnf213 Iapp Mafa Hmcn1 Pdx1 Glp1r

Herc2 Pcsk1 Cebpb

4.6 Syne2 Slc2a2 Ryr1 Nkx6-1 Slc30a8

Macf1 Neurod1 4.4 Tmem27 0 5,000 7,000 9,000 8,000 4,000 6,000 3,000 1,000 2,000 Ranked proteins Figure 2 | The experimental system. (a) Schematic representation of the experimental strategy applied to analyse the proteome of Min6 cells and pancreatic islets. (b) Protein coverage in beta cells (orange bars) and islets (green bars). Each bar represents a selected category, for which the number of corresponding protein coding in the mouse genome is considered as 100%. (c) Ranked protein abundances from highest to the lowest in Min6 cells and islets. (d) Copy numbers of protein markers of the different cell populations of islets. regulatory sites described in the literature suggested a very high high concentration of glucose for 10 min increased the activity of degree of coverage of the functional phosphoproteome. To assess the Mapk1/3 kinases, as well as Foxk1, p70S6K (Rps6kb1) and this more objectively, we compared the phosphosites quantified in pS6 (Rps6), considered hallmarks of the RAS-ERK and mTOR our study with those reported in the PhosphoSitePlus database21, pathways, respectively (Fig. 3d(a)). In the same way, we also which revealed 20,209 identical sites—a high proportion verified that the drug treatments affected the activity of their considering the depth of our analysis (Supplementary Fig. 3E). targets. As expected, cAMP as well as GLP-1 increased the activity Strikingly, about 20% of these phosphosites (4,235 out of these of PKA (Fig. 2d(b,c)); cGMP triggered PKG kinase (Fig. 3d(f)); 20,209) were significantly regulated by drug and glucose the GSK3 inhibitor SB216763 decreased the phosphorylation of treatments (ANOVA test FDRo0.05), as revealed by our the GSK3 substrates (Fig. 3d(d)); and Carbachol treatment MS-based approach (Supplementary Data 3). Remarkably, so triggered PKC activity (Fig. 3d(e)). Kinase-substrate motif far o3% these sites (329 out of 20,209) have been annotated as enrichment analysis of the phosphosites was significantly ‘regulatory sites’, revealing that the vast majority of modulated with respect to the control, independently verifying phosphorylation sites quantified here have not been investigated these findings in each case (Supplementary Fig. 4; t-test, in terms of their functional role or upstream cognate kinase. Of FDRo0.05). The efficiency of the ATP and Glibenclamide the 48,000 previously uncharacterized, high-confidence phos- treatments, which do not directly target a kinases, was confirmed phorylation sites, a similar proportion (17%) were regulated by at by monitoring the phosphorylation of Ptpra, a substrate of CaMK least one of the drugs (ANOVA test FDRo0.05), suggesting an (Fig. 3d(g,h)). even larger scope of functional sites. To assess whether glucose stimulation triggered the activity of signalling pathways known to play important roles in glucose- Unbiased phosphoproteomics discriminates between drugs. mediated insulin secretion in our system, we extracted the To investigate whether this large-scale multivariate phospho- phosphorylation sites on the activation loop of kinases of the proteomics data could enable unsupervised classification of MAPK and PI3K-AKT pathways. The exposure of beta cells to a small molecule compounds according to their targets, we next

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ab d Murine pancreatic beta cells (min6) Drug Effect Ref LG10’ HG10’ HG30’ Glucose treatment with 7 insulin Mapk1 T183 Activates PKA by secretion stimulatory drugs 8-bromo-cAMP cAMP RAF/MAPK Mapk1 Y185 increasing cAMP level Ctr a. Glucose Mapk3 T203 GLP1 GLP1 GLP1R agonist GLP1 Foxk1 S427

8-Bromo-cAMP GSK3i PI3K/AKT Log2 ratio SB216763 GSK3 inihibitor Rps6kb1 S447 lucose-starv) g SB216763 MR agonist, activates ( Carbarchol Carba Rps6 S235 ATP PKC through PLCg Activates PKG by Glibenclamide 8-bromo-cGMP cGMP Carbachol increasing cGMP level b. cAMP Prkaca S235 Depolarization of + cAMP 8-Bromo-cGMP Glibenclamide Glibe cAMP K+ channel GPL1R Prkaca S235

0’ c. GLP1 ATP Activation of P2XR ATP + GLP1

Dpysl2 T514

10’ Low Glu d. GSK3i GSK3 Ctnnd1 S252 10’ High Glu 30’ High Glu 25 experimental conditions in Dpysl2 T509 3–4 biological replicates + GSK3i PLCg c e. Carba MR Prkcd S643 60 Ctr HG 10’ + drugs HG 30’ + drugs LG 10’ + drugs + Carba Cell lysis and EasyPhos method 50

O f. cGMP PKG Bad S113

P 40 TiO2 P O O + cGMP P 30 Ca2+ g. Glibe Kchan Ptpra S180

20 Log2 ratio (drug&glucose-glucose) + Glibe (relative units)

Secreted insulin 10 Ca2+ h. ATP P2XR Ptpra S180 LC–MS/MS analysis 0 + ATP

0' LG 10' HG 10' HG 30' –2 2 LG 10' ATP HG 10' ATP HG 30' ATP LG 10' Glibe HG 10' Glibe HG 30' Glibe LG 10' GLP1 LG 10' cAMP

HG 10' GLP1 HG 30' GLP1 LG 10' Carba Log2 intensity LG 10' cGMP HG 10' cAMP HG 30' cAMP HG 10' Carba HG 30' Carba LG 10' GSK3i HG 10' cGMP HG 30' cGMP HG 10' GSK3i HG 30' GSK3i 35,058 phosphosites quantified 28,637 class I phosphosites quantified

Figure 3 | Experimental workflow for mapping insulin secretion pathways. (a) Schematic representation of the experimental strategy applied to analyse the changes in phosphoproteome profiles of Min6 beta cells after exposure for 10 or 30 min of low (2.5 mM) or high (16.7 mM) concentration of glucose stimulation in combination with the indicated drugs. (b) Drugs used in the study. (c) Amount of secreted insulin (a.u.) after drug and glucose stimulation measured with an Elisa assay. Median and s.d. of triplicates are shown as a bar graph. (d) Glucose stimulation and drug treatment impair the activity of targeted kinases/pathway. performed principal component analysis (PCA) of the 22,241 quantitative phosphoproteomes classify these drugs into three phosphosites quantified in at least 50% of our experimental main groups, reflecting their generally similar effects on the conditions. PCA segregated the drugs into three main clusters: pathways represented by three key kinases: PKA, PKC and CK2A the first includes drugs increasing the activity of PKA (PKA and (Fig. 4f). GLP1R agonists) together with the GSK3 inhibitor; the second consists of drugs triggering the PKC and PKG kinases (PKC and PKG activators); and the third includes ATP and Glibenclamide, Mapping signalling pathways triggered by drug treatment.To which increase intracellular calcium concentration (Fig. 4a). The decipher how key signalling pathways are modulated by the fact that a quantitative data set of more than 22,000 phosphosites drugs, we focused on the 6,040 phosphosites significantly clearly segregates by mode of action implies that compounds modulated in an ANOVA test at a FDRo0.05 and investigated triggering the same kinase/molecule produce similar effects on whether these were significantly enriched for GO-Biological global phosphorylation. Remarkably, the kinase substrate motifs processes (Supplementary Fig. 5B,C) and phosphorylation motifs enriched in the loadings—the phosphosites most responsible for annotated in the Human Protein Reference Database (HPRD)23. the observed segregation in the PCA—almost always mirrored the This analysis confirmed the kinases that we had previously found expected relationships between drugs and targets, with the to be modulated by the compounds. Interestingly, it also revealed additional identification of enrichment of a CK2A substrate motif a strong enrichment for substrate motifs of kinases regulating key in connection with the Glibenclamide and ATP compounds pro-survival and cell cycle-related pathways, such as MAPK, (Fig. 4b,c). While PKA and PKC are believed to be essential for RAF1, AKT, PIM1 and CDK1/2/3 (Fig. 5a; Supplementary GSIS22, to our knowledge this is the first time that the increased Fig. 5B) (Fisher exact test, FDR o0.07). activity of the CK2A kinase is correlated with enhanced insulin To investigate these observations in greater detail, we applied a secretion. Consistently, the MS-based proteomic profile of NOD recently developed strategy24 overlaying our phosphoproteomics diabetic islets also revealed a significant reduction of CK2A levels data onto a literature-derived signalling network that we extracted (Supplementary Data 1; Fig. 1d), highlighting its importance in from the PhosphoSite plus database (Supplementary Fig. 6)21.We GSIS regulation further filtered this network by maintaining only relationships Independent analysis using a correlation matrix (Supple- between proteins that our MS-based proteomic data indicated to mentary Fig. 5A), as well as unsupervised hierarchical clustering be expressed in beta cells (Supplementary Data 2). Using this beta of the global phosphoproteome (Fig. 4d) further confirmed the cell-specific signalling network as a scaffold, we overlaid the classification of the compounds affecting insulin secretion into changes at the phosphoproteome level induced by drug treatment these three main groups. Finally, we verified that the activity of to visualize how the three different classes of drug differentially kinases, whose substrate motifs were enriched in our analysis, modulate key signalling networks. (Fig. 5b; Supplementary Fig. 6). was regulated according to the grouping described above across While PKA activation (orange nodes) is correlated with increased the many different treatment conditions (Fig. 4e). Thus, the JNK signalling, drugs triggering the PKC and PKG kinases as well

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LG 10' Carba HG 30' cGMP 100 HG 10' Starvation Group 2 50 cAMP GLP1 0 GSK3i Group 3

Group1 Glibe

Component 2 (10.2%) –50 Controls LG 10' + ATP HG 30' + ATP HG 10' + ATP LG 10' + Glibe HG 10' + Glibe HG 30' + Glibe LG 10' + GLP1

ATP LG 10' + cAMP LG 10' + Carba HG 30' + GLP1 HG 10' + GLP1 LG 10' + cGMP HG 10' + cAMP HG 30' + cAMP HG 30' + Carba HG 10' + Carba LG 10' + GSK3i HG 30' + cGMP HG 10' + cGMP –100 HG 30' + GSK3i HG 10' + GSK3i 22,241 phosphosites –150 –100 –50 0 50 100 150 Component 1 (32%) be Z-score 4 log2 intensity

2 Group 2 –2 2 LG 10' + GLP1 HG 10' + GLP1 HG 30' + GLP1 LG 10' + GSK3i HG 10' + GSK3i HG 30' + GSK3i LG 10' + cAMP HG 10' + cAMP HG 30' + cAMP LG 10' + Carba HG 10' + Carba HG 30' + Carba LG 10' + cGMP HG 10' + cGMP HG 30' + cGMP LG 10' + ATP HG 10' + ATP HG 30' + ATP LG 10' + Glibe HG 30' + Glibe HG 10' + Glibe Group1 0 Prkaca(T198) Prkcd(S643) –2 Pea15(S116) Component 2 (1e-2) Gap43(S193) Casein kinase 2A –4 Group 3 substrates Hdac2(S394) –3 –2 –1 0 1 2 Tp53(S386) Component 1 (1e-2) cf Group1: PKA motif Group2: PKG, PKC motif Group1 Group 2 Group 3 Carba 2.0 0.8 ATP GLP1 GSK3i Glibe 1.5 0.6 0.4 1.0 R RS Bits 0.2 KE S S PSE R G S Bits K K EG D PP E MR E G TDS SDD PS EK G S K R R R DRAEK S SQH K P S K ENPR K E N R D K+ channel P2YR D N QQND DQG NQ N N M QN TQ FMN H RQ R HHQY HHH HH WWC W M M W M MW MYM M H M Y Y I I FF TF M MIIV P IYE D F P YI I P IV K TT VNQ CN TA P TT VV T T TD VLVN AL F R L L V LH A L ETV V T KL G AVA LAF L GA A K A L GP PA SV 0.0 EG Y 0.5 LA IG L GLP1R GSK3 Q R R S L K EE V KR D D ED SSS K D E K S TS QTGLL S EE R K V K A ND K E S G S LE E P E R D D –0.2 Q KQ F H V Q P R H HCF MM H MWC C MK N P C NN K TIQ Q N P F HI GG GP Q T MI FA cGMP KF YG A GPDGT I Q PT LVL NV V I K LV TN Q T NG R LA A L AL 0.0 Q V G A D D E N L K E G G A SA PLCg –0.4 2 4 6 8 10 12 2 4 6 8 10 12 cAMP Ca2+ Group3: CK2A motif ? ? 2.0 DAG PKG activation 1.5 PKA activation .0 PKC activation 2A activation Bits 0.5 D E E E D E E S E E S E REED E D D SSSG S DK E S D D D S D KRRR P TDKKD SG PPP PG QQQQQH PR YW YH MH M MSMM H M P H H SM H S N H H V II TVN QFF IN G R R GAP NNQ L KPPNIF LGVT IT QQT R AAL KQH K LLTQ RR V QNN L A KY T G RIKPT V AN PLA VLRLR V Global phosphorylation K 0.0 AVTVV K LDLPLVA EA L N TKAG KVAQ GSGGTG A A G 2 4 6 8 10 12 profile Figure 4 | Phosphoproteomics data elucidate the relationships between different drugs. (a) Principal component analysis (PCA) of Min6 beta cells treated with different drugs reveals three clusters. Negative controls cluster together and are separated from drug treated cells. (b) Loadings of A reveal that the phosphosites responsible for driving the segregation in component 1 and 2 are significantly enriched in (c) specific kinase substrate motifs (FDRo0.05). (d) Unsupervised, hierarchical clustering (Pearson correlation distance) of the log2 intensity of 22,241 phosphorylation sites. (e) Heat map of phosphorylation levels of the indicated phosphosites upon drug and glucose treatments. (f) Schematic representation of the effects of drug treatments in beta cells. as modulating calcium concentration (blue and green nodes) Discovering functional nodes controlling the glucose response. activate both AKT and MAPK1/2 signalling pathways. While To extract new functional phosphosites involved in controlling these connections have been described previously, although under insulin secretion, we started with the 6,040 ANOVA significantly different cellular contexts25–28, our data now provide a global and modulated sites (Supplementary Data 4), and selected only those quantitative phosphoproteomic background to the mechanisms peptides whose phosphorylation was increased after glucose involved. stimulation, and was also further enhanced by treatment with Interestingly, we found that all the compounds employed all of the compounds tested (Fig. 6a). This strategy revealed trigger pathways that converge upon activation of cell cycle- 64 phosphosites on 63 proteins to be hyperphosphorylated by related kinases, such as Cdk1, Cdk2, Cdk5 and Cdk7. This is drug treatments. Remarkably, many of the identified phospho- consistent with our kinase–substrate motif enrichment analysis proteins are involved in processes expected to be triggered by (Fig. 5a), which indicated that these compounds may also affect glucose stimulation, such as vesicle trafficking, the regulation of cell cycle progression in addition to increasing insulin secretion. calcium channels and the release of insulin granules. Thus analysis of our deep phosphoproteome provide a molecular Next, we decided to investigate the temporal regulation of these framework for the findings that glucose in conjunction with sites and of the global beta cell phosphoproteome in a glucose- certain diabetic drugs can act as a ‘mitogenic hormone’ in beta dependent time course in insulin-secreting cells. We again cells and providing a molecular underpinning to the observed applied the EasyPhos pipeline with increased time resolution connection between insulin secretion and of glucose administration, with eight time points spanning signalling networks29–31. from 2.5 min to 1 h (Fig. 6b). Consistent with our previous

6 NATURE COMMUNICATIONS | 7:13250 | DOI: 10.1038/ncomms13250 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13250 ARTICLE

a b-Adrenergic receptor kinase MAPKAPK1 kinase Cdc2 kinase Casein kinase II Calmodulin-dependent protein kinase II G protein-coupled receptor kinase 1 PKC kinase PKC epsilon kinase MAPK1/2 kinase RAF1 kinase Pyruvate dehydrogenase kinase PKA kinase Growth associated histone HI kinase CLK1 kinase CDK1,2,4,6 kinase CDK kinase BARD1 BRCT domain binding motif Akt kinase Pim1 kinase substrate sequence MDC1 BRCT domain binding motif ATM kinase AMPK 14–3–3 domain binding motif Casein kinase I PAK2 kinase Aurora-A kinase Starv LG 10' HG 10' HG 30' ATP Glibe Carba cGMP GLP1 GSK3i –1 01 cAMP Stimulus Enrichment score Ca2+/PKC Ca2+ PKC/PKG PKA/PKC PKA drugs High Glu Low Glu Starvation PKG drugs drugs PKG drugs

b Glibe GLP1

ATP

2+ Ca /ATP P2Y-P2X GLP1R

+ channel

K Ca2+ channel receptor

Intracellular GSK3i cGMP cAMP Ca2+

T287 Plcl1 Prkg1 S660 Gsk3b S96 S386 Tp53 CK2A Camk2a Prkaca Prkca/b Th S40 S473 S9 S128 S16 S63 S160 M3 Trim28 Carba S595 S308 S104 Bad Pdk1 S116 receptor Stmn1 Tbc1d4 Marcks Akt1 S369 S948 S128 S351 S21 Mettl1 Pea15 S580 Cdc25b Jnk1/2 S939 S140 p38a/d Rps6ka3 S422 Raf1 S727 H2afx S206–403 Eif4b S38 mTOR pathway Tsc2 Stat3 Prkd1 JNK substrates S181 Ptk2 Msk1 S25 mTOR S395 Wipi2 Ppp1r1a Mek1/2 S67 S389 S20–S24 Pgrmc1 Cdk5 S1692 S1247 Mcm3 Incenp T418 S535 Foxk2 Coro1a S339 S427 Hmgn1 T719 T388 Cdc42bpb Rrm2 S198 S339 Map1b p38 pathway S20 S332 S447 Cdk1 T487 Rc3h1 Ezh2 Ndel1 Dcx Foxk1 T1784 p70S6K T109 H3f3a S29 Mapk1/2 S164 S235-236 S22 S1002 S725 Prrc2a Lmna S100 Mcm4 Rps6 S104 S445 S366 Supv3l1 S751 Cdk7 T950 S575 T53 S2223 Jun S1280 Phldb1 T221 S3 Cell cycle Larp1 S460 S38 Safb S164 Hnrnph1 S328 Cdk4 Stk10 Stim1 Atf2 Tpr Cdk2 Mybbp1a S800 Rps3 Rb1 Klc1 Fam207a MAPK pathway Dlgap5

Phosphorylated by PKA drugs Activation Direct Phosphorylated by PKC/PKG drugs Inhibition Indirect Phosphorylated by Ca2+ drugs

Figure 5 | Global pathways regulation in response to drug treatments. (a) Heat map of kinase substrate motif enrichment (FDRo0.02) in glucose and drug-induced phosphoproteomes. (b) Phosphoproteomics data were mapped onto a global naive network of signalling information and then filtered (Supplementary Material). Nodes were colour coded according to the class of compounds responsible of their phosphorylation.

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ab 2 Glucose stimulation of Min6 beta cells Early Intermediate Late 1 0’ 2.5’ 5’ 10’ 15’ 20’ 30’ 60’

0

–1

ANOVA DFR<0.05 –2 Quantification of MS-based 6,041 glucose & drugs

modulated phosphosites insulin secretion phosphoproteome analysis Z-score phospho STY intensity Ctr ATP Starv Glibe carba GLP1 cAMP cGMP GSK3i 15,187 phosphosites + Glucose 13,556 class 1 phosphosites Intermediate response c 40 Novel regulatory sites 20' 20 15' 10' 2.5' 0' 5' 10' 15' 20' 30' 60' 30' 60' Brd2(S297) 0 Ccnl1(S341) Late response Chgb(S100) –20 Crebbp(S120) 5' Dnmt3a(S4) Dync1i2(S95) –40 2.5' Eif2s2(S2) Early response Eif4enif1(S352) –60

Fryl(S1958) Component 2 (16.2%) Gfpt1(S243) –80 0' Gramd3(S57) Control Hn1(S87) Hnrnpd(S83) –100 –80 –60 –40 –20 0 20 40 60 80 100 Itsn2(S838) Component 1 (49.3%) Lbr(S103) Mcoln1(S547) Nbeal2(S2727) Pitpnc1(S299) d Rab11fip1(S340) Drug treatments Time course Ralgapa1(S796) 4 3 Rps6ka3(S369) 3 2 Scmh1(S574) 1 Scrib(S1218) 2 Slc24a2(S334) 1 0 Slc24a2(S337) –1 Sorbs1(S58) Fold change 0 Fold change Srrm1(S572) –2 Srrm2(S1339) 3 Tmem115(T329) 4 Tomm22(S15) 3 2 Top2b(S1539) 1 Zfp185(S317) 2 1 0 Z-score

Fold change –1

–20 2 0 Fold change Phosphosite intensity –2 3 4 2 e 3 1 2 0 1 Healthy Pre- diabetic Diabetic –1 Fold change 0 Fold change Gfpt1 –2 Nbeal2 4 3 Scrib 3 2 Gramd3 2 1 Srrm2 1 Brd2 0 0 –1 Chgb Fold change Fold change Rps6ka3(S369) Hnrnpd(S83) Gfpt1(S243) Eif2s2(S2) Srrm1 Ctr –2 ATP

Glibe 2.5' 5' 10' 15' 20' 30' 60' GLP1 cAMP Carba Crebbp cGMP GSK3i Fryl Z-score –20 2 LFQ protein intensity

Figure 6 | Identification of new functional sites controlling insulin secretion. (a) Phosphorylation profile of the potential regulatory phosphosites. (b) Experimental strategy applied to profile the phosphoproteome in a glucose time course experiment. Lower part: PCA of Min6 beta cells treated for the indicated time points with glucose. (c) Heat map of the Log2 LFQ intensities of the potential regulatory phosphosites significantly modulated in the time course experiment. (d) For the four known regulatory sites, the median fold-changes and s.e.m. compared with starvation control cells are plotted. (e) Heat map of the Log2 LFQ intensities of 10 novel regulatory phosphoproteins significantly modulated in pre-diabetic and diabetic NOD islets. experiments, glucose stimulation increased the amount of demonstrated high reproducibility at each time point (Pearson secreted insulin in a time-dependent manner, with peak insulin correlation coefficients 0.85–0.95) (Supplementary Fig. 7B). secretion occurring after around 30 min (Supplementary Fig. 7A). In a PCA, component 1 clearly segregated the control and early For subsequent analysis, we focused on around 11,000 sites for (0 and 2.5 min) time points from intermediate (5, 10 and 15 min) which we had quantitative values for at least four of the eight time and late (20, 30 and 60 min) time points (Fig. 6b). Unsupervised points (Class 1 sites, Supplementary Data 5). As with our hierarchical clustering of the significantly modulated phospho- previous phosphoproteome measurements, biological replicates peptides (ANOVA, FDR o0.05) revealed four predominant

8 NATURE COMMUNICATIONS | 7:13250 | DOI: 10.1038/ncomms13250 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/ncomms13250 ARTICLE groups of phosphosites with different dynamic profiles (Supple- partners (Fig. 7a,b), we decided to investigate its effect on the mentary Fig. 7C). Sustained glucose responders were enriched for gene expression profile at the level of the proteome. To minimize PKA and PKC substrate motifs, while the transient responders potential off-target effects of the overexpression, we performed were enriched for AKT and p70S6K kinase substrates, consistent our experiments in two biological systems, Ins1e and HEK293 cell with literature reports32,33. lines, which express the exogenous Dnmt3a at low and high Of the 64 phosphosites universally regulated by drug treatment levels, respectively. In both systems, we ectopically expressed the in a glucose-dependent manner, 34 were also significantly wild type or the unphosphorylable mutant form of the regulated by glucose in the time-series studies (Fig. 6c). methyltransferase and compared the proteomes (Supplementary Discarding two sites that are not conserved between mouse and Fig. 10C–G; Supplementary Data 6). Overexpression of wild-type human, our combined strategy resulted in a high stringency list of DNMT3A led to downregulation of a set of proteins involved in 32 potential regulatory phosphosites of which only four had cell proliferation and insulin signalling regulation (Fig. 7c,g). In previously been characterized as being functional (Fig. 6d). One contrast, over-expression of the S7A mutant of DNMT3A of the known sites is S243 of the metabolic glutamine- resulted in drastic upregulation of this set of proteins (mean fructose-6-phosphate transaminase (Gfpt). Phosphorylation of fold change of more than three), compared with the wild-type this residue controls the activity of the enzyme, which so far has form (Fig. 7d). These results, confirmed in the human cell line been characterized as a regulator of glucose flux into the Hek293 (Supplementary Fig. 10D,E; Supplementary Data 7), hexosamine pathway only in the brain34. The finding that this demonstrate that the phosphorylation of S7 in response to site is regulated in the beta cell suggests that this may similarly be glucose contributes to the regulation of genomic DNMT3A an important control point for glucose metabolism in the context targets via phosphorylation dependent interactions. of insulin secretion. A large proportion of these phosphosites To determine the proportion of gene regulatory events is involved in the regulation of vesicle trafficking and cell cycle- mediated via regulated phosphorylation of S7 of DNMT3A, we related functions, providing molecular detail on the connection compared the proteomes of beta cells stimulated or not by glucose between insulin secretion and cell proliferation. Among these for 6 or 12 h (Supplementary Data 8; Supplementary Fig. 11). Out proteins were two poorly characterized calcium channels, Slc24a2 of 7,000 proteins, about 1,500 were significantly downregulated and Mcoln1, both localized to the endoplasmatic reticulum. We and this set encompassed 65% of the DNMT3A-suppressed genes found that overexpression of the unphosphorylable mutants of (Fig. 7e). Conversely, DNMT3A is involved in 5% of the gene both of these proteins impairs the ability of the beta cells to regulatory events induced by glucose. Together, these observa- secrete insulin upon glucose stimulation (Supplementary Fig. 8). tions establish that S7 phosphorylation of DNMT3A represents a Remarkably the protein level of 10 out of these 32 novel pathway through which glucose suppresses the expression of an regulatory sites is significantly impaired in NOD pre-diabetic and important subset of target genes (Fig. 7f). diabetic islets (Fig. 6e).

Discussion Characterization of the S7 DNMT3A phosphorylation. Among Beta cell dysfunction is a major hallmark of the progression of the new potentially functional phosphosites (Supplementary diabetes. Comprehensive identification of the molecular mechan- Fig. 9A), we also identified Serine 4 of the de novo DNA methyl- isms triggered by glucose stimulation and governing insulin DNMT3A (Fig. 6c; Supplementary Fig. 9B,C). This secretion is thus crucial not only for a deep understanding of this protein plays a crucial role in beta cell differentiation and process but also for the characterization of existing and metabolism. Beta cell-specific deletion of DNMT3A is sufficient development of novel effective therapeutics targeting this disease. to cause beta-to-alpha-cell reprogramming, driving a metabolic Here, we applied state of the art, high-resolution MS-based program by repressing key genes to enable the coupling of proteomics to quantitatively describe the phosphorylation events insulin secretion to glucose levels during beta cell maturation35,36. occurring in murine beta cells actively secreting insulin. We first Additionally, a genome-wide association study (GWAS) robustly profiled the proteomes of NOD mice at different diabetic stages. revealed DNMT3A as one of the genetic contributors to the Then we functionally characterized our experimental system by pathogenesis of type 1 diabetes37. To investigate the functional comparing the proteomic profiles of islets and beta cells to a role of this phosphorylation site, we employed interaction depth of 49,000 proteins. Apart from validating our cell line proteomics of the human protein, where the corresponding site system for our purposes, our copy-number estimates of islet occurs at S7 of DNMT3A. proteins will be a useful resource to the community. We coupled immunoprecipitation experiments of the wild-type We quantified changes in the phosphorylation status of form of the methyltransferase, as well as the unphosphorylable 428,000 Class 1 phosphosites upon glucose stimulation and S7A mutant, to quantitative MS-based proteomics38,39. Stati- treatment with diverse drugs affecting insulin secretion. These stically significant interactors of wild type DNMT3A included a data classified the insulin secretion-modulating compounds into histone deacetylase (HDAC2) and histones (ex. histone H3.1), as three groups, representing key nodes in the beta cell signalling expected from the literature40,41 (Fig. 7a). Remarkably, we found network targeted. Through this analysis, we validated known that these interactions were significantly decreased following drug–kinase–substrate relationships, and also identified new mutation of this regulated phosphorylation site (Fig. 7b). Both the molecular targets by which compounds regulate insulin secretion. wild-type form and the unphosphorylable mutant are correctly For example, this approach revealed that the localized in the nucleus, therefore the differential interaction is (CK2) substrate motif is strongly enriched in beta cells treated not due to aberrant subcellular localization (Supplementary with Glibenclamide and ATP. Both these drugs increase insulin Fig. 10A). We also confirmed the role of S7 phosphorylation in secretion by augmenting the calcium response42,43. The regulating the association of the methyltransferase to its partners combination of our phosphoproteomic data set with known in a human cell line, HEK293 (Supplementary Fig. 10B,C). kinase–substrate relationships, extracted from PhosphositePlus, Recently it has been shown that the histone H3-DNMT3A enabled us to connect the drug-dependent increase in calcium interaction triggers activation of the methyltransferase after concentration with downstream activation of CK2. Our study its initial genomic positioning41. Given the role of the S7 suggests that CAMK2 kinase, which is sensitive to intracellular phosphorylation in modulating the DNMT3A association with its calcium levels, serves as a molecular bridge between the CK2

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acb 4 4.5 Histone H4 5 3.5

Histone H4 4 3.54 3 Tcf20 -value

-value Gpt2 P HNRPD 2.5 -value P 3 S100A3 TOP2A 2.53 P

-test 2 DNMT3A Histone H2A t -test Histone H2A t Histone H3.1 Histone H3.1 2 -test HDAC2 1.52 t 1.5

-Log DNMT3A

-Log HDAC2 1 -Log 1 0.51

0 0 DNMT3A 0.5

0 1 2 3 4 –4 –3 –2 –1 1 4.5 –2 –1 1.5 0 Log2 t-test difference –2.5 –1.5 –0.5 0 2 4 glucose- Ctr Log2 t-test difference –4 –2 Dnmt3a S7A- WT Log2 t-test difference Dnmt3a WT- Ctr defg 1 2 Glucose GO BP Kegg PCC= –0.75 PCC= 0.35 50 40 0 1 Kinase 30 Rad23b Rsl1d1 Rbmxrtl Tmx2 0 Histones 20 –1 Tial1 Zcchc8 Atp6v0c Cdc23Nup160 Tial1 Cuta Taco1 H2A H2B 10 Enrichment factor Anapc5 Gtf3c1 HDACs LOC100910831 –1 P –2 Mcm6 Gpt2 H4 H3 Dnmt3a 0 Tcf20 Tcf20 Cdc23 Zcchc8 –2 Mcm6 Gene expression Nmt1 –3 Dtd1 Chuk modulation Fuca2 –3 Prkag1

Gpt2 ErbB signalling Insulin signalling Fold change log 2 Dnmt3aWT-Ctr –4 Fold change log2 6h glucose-Starv Cuta Anapc5 –4

Lsm1 Adipocytokine signalling Gtf3c1 –5 –5 –4 –3 –2 –1 0 1 23 4 5 –5 –4 –3 –2 –1 0 1 mediated proteolysis

Fold change log2 Fold change log 2 Dnmt3aWT-Ctr Negative regulation of cell cycle Dnmt3a S7A-WT

Figure 7 | The DNMT3A phosphorylation of S7 affects the gene expression profile of beta cells. (a) Volcano plot of MS-quantified wild-type DNMT3A interactors in glucose treated or starved Ins1e cells. (b) Significant interactors of DNMT3A. Volcano plot of MS-quantified proteins in wild type and S7A mutant in glucose treated Ins1e cells. (c) Volcano plot of MS-quantified proteins after the overexpression of the wild type form of DNMT3A or empty vector as control, in Ins1e cells. (d) Scatterplot of proteins significantly downregulated after the overexpression of wild type or S7A mutant forms of DNMT3A in Ins1e cells. (e) Scatterplot of significantly downregulated proteins after the overexpression of wild-type form of DNMT3A and stimulation with glucose (16.7 mM) for 6 h in Ins1e cells. (f) Schematic representation of the proposed model. (g) Bar graph representing GO-Biological processes enriched in the proteins that are downregulated by both glucose and DNMT3A overexpression.

kinase and the treatment with Glibenclamide and ATP. Addi- phosphatase activity46. This could be explained by the fact tionally our approach revealed that ATP triggers the activity of that phosphatases activity is rarely correlated with the phos- PKC kinase. This observation is in agreement with the current phorylation level of key specific phosphatase residues47. hypothesis proposing that ATP promotes insulin exocytosis Given the prominent role of autocrine signalling in beta cells, it partly by raising calcium concentration and partly by increasing is important to consider that it is difficult to discriminate whether DAG via PLC pathway44,45. These results illustrate how unbiased the mentioned kinases are activated directly and/or indirectly by MS-based phosphoproteomics can be applied to identify drugs. molecular targets involved in known outcomes as well as those We also generated a quantitative atlas of dynamic protein involved in as yet uncharacterized effect of the drugs. Mapping phosphorylation following glucose stimulation at eight time our phosphoproteomics data onto signalling networks connected points. By integrating this large-scale phosphoproteomics data the drugs tested here to cell cycle related and proliferative with a manually curated insulin secretion pathway, we delineated pathways, which is a desirable effect in view of the fact that type 2 key topological features of this signalling network (Fig. 8). Two diabetes involves progressive failure of beta cells. Further major pathways regulated by glucose are PI3K-AKT and MAPK, experiments are necessary to confirm the pro-proliferative which control nutrient sensing, protein synthesis, metabolism properties of these drugs in islets, which have a low proli- and cell proliferation48. In our quantitative phosphoproteome, we feration rate compared with Min6 cells. observed that in beta cells both these pathways are fully activated Our strategy did not reveal a differential regulation of glucose upon 10 min of glucose stimulation. The temporal resolution and drugs on phosphatase activity. Although protein phospha- of our time-series study enabled discrimination of AKT and tases regulate insulin-secretion pathways, our strategy did mTOR-p70S6K signalling in the context of regulating insulin, not reveal a differential regulation of glucose and drugs on which was delayed with respect to the activation of AKT. This is

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K+

K+ Na+

ATP1A SUR1 Kir6.2 VDCC K-Ca NSCC S554 T378 S986 S385 S2078 S2075 S782 PIP5K1C S1218 S1950 Scrib S328PIK3C2A S340 S1118 S1115 PI4K2A RAB11FIP1 Na+ Myo5a K+ S600 S47 S51 Ca+ S57 GRAMD3 S243 T229 S333 PAK1 GFPT1 HSP pathway ATP CAMK2 T331 PAK4 S181 INS S293 S104 S192 GCK S269 PDHA1 TMEM115 T329 HIPK2 PDX1 GLUT1/2 Pyruvate TCA cycle S82-83 S111 S68-69 RALGAPA1 Glucose S796 Glucose CREB ITSN2 S838 S120 NPY Mito CREBBP PITPNC1 INS S299 VPS4B S339 S198 MAFA S342 S251 S1958 S102 GIP GIP1R PKA S4 FRYL S307 S455 ATF4 INS S14 S62 T365 PIKFYVE ACLY S647 S431 DNMT3A Syntaxin S369 S743 S427 VAMP2 RPS6KA3 RAP1 APAF1 TBC1D10B FOXKB1 GLP1 GLP1R GNAS AC S669 LARP1 S67 S1091 S578 S53 cAMP S666 S751 TAF3 S229 S300 SNAP91 S235 S236 S627 S600 INS S100 PI3K PDK1 AKT TSC2 mTOR RPS6KA1 rpS6 Chgb S124 S939 S240 FOXO1 S284 S297 T183 BRD2 S1535 RIMS2 Y15 T109 ERK1/2 S11 S140 S1115 RAS RAF MEK CDK1 MCM4 S1115 T19 MCM2 S580 Y185 T14 S32 S139 CDK2 T719 MCM3 RIMS2 T393 STIM1 KHDRBS1 EPAC CCNE1 PCLO S617 S575 S58 CCNL1 S1129 S443S596 S128 TOP2B S605 MDC1 S317 T300 S1539 S1263 S168 Acetyl- S497 S368 M3R choline Gq/G11 PLC PKC PKCe Tjp1 RAB3 S384 DAG S10 IP3 S333 T331 CAMK2 FA GRP40 Ca+ S605 Ca+ Syn1 S1832 S934 ITP3 RYR 2.5’5’ 10’15’ 20’ 30’ 60’

S2727 Highest peak of phosphorylation NBEAL2 ER Ca+ SLC24A2 S334

Figure 8 | Time-resolved map of insulin secretion pathways in beta cells. Assembly of insulin secretion pathways from multiple databases. Phosphosites are labeled according to their highest phosphorylation peak, as revealed in our phosphoproteomics data set (see legend). New regulatory phosphoproteins are labelled in green. Proteins significantly modulated in diabetic islets have a red border. consistent with current knowledge of the PI3K-AKT-mTOR cFos, cJun, Stat5, occurring upon 15 or 20 min of stimulation in signalling network, whereby mTOR is downstream of AKT49, and cell line systems54. the significant temporal latency between the activation of AKT Our approach also enabled the identification of new molecular and mTOR50,51. players of the glucose response in murine beta cells. By Upon 10 min of glucose stimulation almost all proteins combining our phosphoproteomics data sets with an in silico involved in the transport and the exocytosis of insulin granules approach, we identified 32 key phosphorylation events occurring were highly phosphorylated. This observation is in line with on 29 proteins as high-stringency candidate functional sites in established models of insulin secretion, whereby in response to mediating the glucose response in beta cells. Strikingly a glucose stimulation, a pool of docked and primed insulin storage significant proportion of these novel regulatory phosphoproteins granules fuse and release insulin, while additional storage were downregulated in NOD pre-diabetic and diabetic mice granules traffic to join the readily releasable pool at the cell (P valueo0.005). This observation further highlights the surface52. These events are triggered by increased cytoplasmic importance of these proteins in the regulation of insulin secretion. calcium concentration resulting from plasma membrane We also identified the DNA methyltransferase, DNMT3A as a depolarization. Consistently we found that upon 10 min of new molecular target of glucose-mediated beta cell signalling. glucose stimulation the voltage-dependent calcium channel was This protein plays an important role in beta cells differentiation: highly phosphorylated and subsequently CAMK2 kinase activated DNMT3A binds Nkx2.2, Grg3 and Hdac1 and mediates the (Fig. 8). These molecular events are elegantly connected to the repression of the Arx gene, thereby preventing beta to alpha cell activation of the PKA kinase, triggered by the generation of conversion36. In addition, it has recently been reported that the cAMP by , which is also highly phosphorylated beta cell–specific deletion of DNMT3A results in loss of the upon 10 min of glucose stimulation, as revealed by our glucose-induced insulin secretion35. Here we show that glucose phosphoproteomics data. stimulation increases the phosphorylation of S4 of DNMT3A Another crucial feature of the beta cell response to glucose (S7 in human DNMT3A). Follow-up experiments using stimulation is the modulation of gene expression profiles through interaction proteomics revealed that this phosphorylation event the activation of different transcription factors53. We were able to mediates the association of the methyltransferase with specific quantitatively measure the phosphorylation profiles of key beta transcription factors and histones. Such interactions are already cell transcription factors, such as Pdx1, MafA, ChREBP and known to play a crucial role in the regulation of the DNMT3A- Foxo1 and found that 15 min of glucose stimulation is sufficient mediated repression of its target genes41. We now demonstrate to trigger their phosphorylation. These data are in line with the that the ability of the methyltransferase to downregulate its EGF-induced phosphorylation of transcription factors, such as targeted genes is regulated through interactions modulated by the

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S7A mutation. Taken together, our results indicate that glucose were electrosprayed and analysed by tandem mass spectrometry on a Q Exactive stimulation downregulates a subset of genes involved in the HF57,58 (Thermo Fischer Scientific) using HCD based fragmentation, which was set regulation of cell cycle and signalling, through the DNMT3A to alternate between a full scan followed by up to five fragmentation scans. Proteome and phosphoproteome data were processed and statistically analysed as phosphorylation of S7. described in Supplementary Methods. Here we have highlighted only a subset of the glucose-driven physiological processes which we found in our data set. We expect that these large-scale MS-based data will serve as a Data availability. The mass spectrometry proteomics data have been deposited to valuable resource for future hypothesis-driven research to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/ cgi/GetDataset) via the PRIDE59 partner repository with the data set identifier investigate as yet unknown molecular mechanisms driving insulin PXD003850. All other data supporting the findings of this study are available secretion in pancreatic beta cells, and that studies of this kind will within the article and its supplementary information files or from the thereby reveal promising new targets for the treatment of diabetes corresponding author upon reasonable request. as well as mechanisms of action of diabetic drugs. References Methods 1. Boyle, J. P., Thompson, T. J., Gregg, E. W., Barker, L. E. & Williamson, D. F. Reagents. Glibenclamide (20 mM), 8-bromo cAMP (10 mM), 8-bromo cGMP Projection of the year 2050 burden of diabetes in the US adult population: (10 mM), SB216763 (40 mM) and ATP (20 mM) were purchased from Sigma. GLP-1 dynamic modeling of incidence, mortality, and prevalence. (10 nM) was provided by Sanofi and Carbachol (100 mM) obtained from European Population health metrics 8, 29 (2010). Pharmacopeia Reference Standard. Flag-M2 beads were obtained from Sigma 2. Weir, G. C., Laybutt, D. R., Kaneto, H., Bonner-Weir, S. & Sharma, A. Beta-cell (A2220) and Flag-DNMT3A from Invitrogen. Anti-HDAC2 antibody was obtained adaptation and decompensation during the progression of diabetes. Diabetes from (cat no. 5113, 1:1,000) and anti-Flag antibody from Sigma 50(Suppl 1): S154–S159 (2001). (cat no. F3165, 1:2,000). Slc24a2 and Mcoln1 constructs from were purchased 3. Sherry, N. A., Tsai, E. B. & Herold, K. C. Natural history of beta-cell function in from Origene. type 1 diabetes. Diabetes 54(Suppl 2): S32–S39 (2005). 4. Schofield, C. J. & Sutherland, C. Disordered insulin secretion in the Cell culture and transfection. Min6 cells were cultured in DMEM medium development of and Type 2 diabetes. Diabetic medicine: a (Glutamax, Gibco) supplemented with 10% fetal calf serum, 100 U ml 1 penicillin, journal of the British Diabetic Association 29, 972–979 (2012). 100 mgml 1 streptomycin, 1 mM sodium pyruvate, 1 M Hepes and 50 mM 5. Nesher, R. et al. Beta-cell protein kinases and the dynamics of the insulin 2-mercaptoethanol. INS-1E cells (kindly provided by Dr Martin Jastroch, response to glucose. Diabetes 51(Suppl 1): S68–S73 (2002). Helmholtz center, IDO, Munich) were grown in a humidified atmosphere 6. Chen, X. Y. et al. Brain-selective kinase 2 (BRSK2) phosphorylation on (5% CO2, 95% air at 37 °C) in monolayer in modified RPMI 1,640 medium PCTAIRE1 negatively regulates glucose-stimulated insulin secretion in supplemented with 10% fetal calf serum, 10 mM Hepes, 100 U ml 1 penicillin, pancreatic beta-cells. The Journal of biological chemistry 287, 30368–30375 100 mgml 1 streptomycin, 1 mM sodium pyruvate, 50 mM b-mercaptoethanol (2012). (all from Gibco) and 0.5% BSA (from Sigma). Hek293 cells were cultured in 7. Han, D. et al. Comprehensive phosphoproteome analysis of INS-1 pancreatic DMEM medium supplemented with 10% fetal calf serum, 100 U ml 1 penicillin, beta-cells using various digestion strategies coupled with liquid 100 mgml 1 streptomycin. Cells were transfected with Lipofectamine 2,000 or chromatography-tandem mass spectrometry. Journal of proteome research 11, 3,000, according to the manufacture protocol. 2206–2223 (2012). 8. Li, J. et al. Quantitative Phosphoproteomics Revealed Glucose-Stimulated Responses of Islet Associated with Insulin Secretion. Journal of proteome Islet isolation. Adult C57BL/6 and NOD mice (Jackson Laboratories, ME) were research 14, 4635–4646 (2015). euthanized by cervical dislocation. The upper abdomen was incised to expose 9. Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phospho- and intestines. Pancreas was perfused through the common bile duct with cold proteomics reveals in vivo insulin signaling dynamics. Nature biotechnology 33, collagenase P (from Roche) in saline solution. The pancreas was dissected and placed into a warm collagenase saline solution for 15 min. After enzymatic 990–995 (2015). digestion of the pancreatic tissue, islet were picked and cultured overnight in an 10. Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. & Mann, M. Minimal, incubator at 37 °C. encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nature methods 11, 319–324 (2014). 11. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, Insulin assay. Cells were grown overnight with DMEM low glucose medium, then individualized p.p.b.-range mass accuracies and proteome-wide protein were washed with Krebs-Ringer-Buffer and incubated with starvation buffer for quantification. Nature biotechnology 26, 1367–1372 (2008). 90 min. Cells were then incubated with high glucose medium (Krebs-Ringer-Buffer 12. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant supplemented with glucose 16.7 mM and BSA 0.05%) or low glucose medium environment. Journal of proteome research 10, 1794–1805 (2011). (Krebs-Ringer-Buffer supplemented with glucose 2.5 mM and BSA 0.05%). 13. Ize-Ludlow, D. et al. Progressive erosion of beta-cell function precedes the onset Aliquots of the supernatant were assayed for the amount of insulin (insulin of hyperglycemia in the NOD mouse model of type 1 diabetes. Diabetes 60, assay from Cisbio), according to the manufacture protocol. 2086–2091 (2011). 14. Ishihara, H. et al. Pancreatic beta cell line MIN6 exhibits characteristics of Immunoprecipitations. For immunoprecipitations, cells were lysed in ice-cold glucose metabolism and glucose-stimulated insulin secretion similar to those of NP-40 extraction buffer (50 mM Tris-HCl, pH 7.5, 120 mM NaCl, 1 mM EDTA, normal islets. Diabetologia 36, 1139–1145 (1993). 6 mM EGTA, 15 mM sodium pyrophosphate and 1% NP-40 supplemented with 15. Skelin, M., Rupnik, M. & Cencic, A. Pancreatic beta cell lines and their protease and phosphatase inhibitors (Roche) and clarified by centrifugation at applications in diabetes mellitus research. Altex 27, 105–113 (2010). 14,000 r.p.m. Supernatants were incubated at a concentration of 30 ml of resin per 16. Pan, C., Kumar, C., Bohl, S., Klingmueller, U. & Mann, M. Comparative mg lysate over night with Flag-M2 beads (Sigma) previously washed 3 times with proteomic phenotyping of cell lines and primary cells to assess preservation of PBS. Beads were then washed 3 times with lysis buffer and 3 times with This-HCl cell type-specific functions. Molecular & cellular proteomics: MCP 8, 443–450 50 mM pH 8.5 On bead digestion of protein complexes used for MS analysis was (2009). performed38,39,55. Peptides were eluted, desalted and analysed by LC–MS/MS. 17. Kim, A. et al. Islet architecture: A comparative study. Islets 1, 129–136 (2009). 18. Wisniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A. ‘proteomic ruler’ for protein copy number and concentration estimation without spike-in standards. Proteome and phosphoproteome sample preparation. Cells were lysed in Molecular & cellular proteomics: MCP 13, 3497–3506 (2014). GdmCl buffer. Proteome preparation was done using the in StageTip (iST) 19. Mourad, N. I., Nenquin, M. & Henquin, J. C. Metabolic amplifying pathway method10. Large-scale phosphoproteome preparation was performed as previously 9 increases both phases of insulin secretion independently of beta-cell actin described . A limited amount of material (1 mg per condition) was lysed, alkylated microfilaments. American journal of physiology Cell physiology 299, C389–C398 and reduced in one single step. Then proteins were digested and phosphopetides (2010). enriched by TiO beads. After elution, samples were separated by HPLC in a single 2 20. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct run (without pre-fractionations) and analysed by mass spectrometry. regulatory nature of Tyr and Ser/Thr-based signaling. Cell reports 8, 1583–1594 (2014). Mass spectrometric analyses. The peptides or phosphopeptides were desalted on 21. Hornbeck, P. V. et al. PhosphoSitePlus: a comprehensive resource for StageTips56 and separated on a reverse phase column (packed in-house with investigating the structure and function of experimentally determined post- 1.8-mm C18- Reprosil-AQ Pur reversed-phase beads) (Dr Maisch GmbH) over translational modifications in man and mouse. Nucleic acids research 40, 270 min (single-run proteome and phosphoproteome analysis). Eluting peptides D261–D270 (2012).

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Fuks, F., Burgers, W. A., Godin, N., Kasai, M. & Kouzarides, T. Dnmt3a binds Additional information deacetylases and is recruited by a sequence-specific repressor to silence Supplementary Information accompanies this paper at http://www.nature.com/ transcription. The EMBO journal 20, 2536–2544 (2001). naturecommunications 41. Guo, X. et al. Structural insight into autoinhibition and histone H3-induced activation of DNMT3A. Nature 517, 640–644 (2015). Competing financial interests: This study was done as a collaboration between the 42. Khan, S. et al. Autocrine activation of P2Y1 receptors couples Ca (2 þ ) influx to Max-Planck Institute of Biochemistry and Sanofi-Aventis Deutschland (SAD), who Ca (2 þ ) release in human pancreatic beta cells. Diabetologia 57, 2535–2545 provided a grant for this purpose to MPIB. M.M, A.S, T.K & M.S. are employed at Sanofi. (2014). 43. Varadi, A. et al. Intracellular ATP-sensitive K þ channels in mouse pancreatic Reprints and permission information is available online at http://npg.nature.com/ beta cells: against a role in organelle cation homeostasis. Diabetologia 49, reprintsandpermissions/ 1567–1577 (2006). How to cite this article: Sacco, F. et al. Glucose-regulated and drug-perturbed 44. Wuttke, A., Yu, Q. & Tengholm, A. Autocrine Signaling Underlies Fast phosphoproteome reveals molecular mechanisms controlling insulin secretion. Repetitive Plasma Membrane Translocation of Conventional and Novel Protein Nat. Commun. 7, 13250 doi: 10.1038/ncomms13250 (2016). Kinase C Isoforms in beta Cells. The Journal of biological chemistry 291, 14986–14995 (2016). Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in 45. Wuttke, A., Idevall-Hagren, O. & Tengholm, A. P2Y(1) receptor-dependent published maps and institutional affiliations. diacylglycerol signaling microdomains in beta cells promote insulin secretion. FASEB journal: official publication of the Federation of American Societies for Experimental Biology 27, 1610–1620 (2013). This work is licensed under a Creative Commons Attribution 4.0 46. Sacco, F. et al. Mapping the human phosphatome on growth pathways. International License. The images or other third party material in this Molecular systems biology 8, 603 (2012). article are included in the article’s Creative Commons license, unless indicated otherwise 47. Sacco, F., Perfetto, L., Castagnoli, L. & Cesareni, G. The human phos- in the credit line; if the material is not included under the Creative Commons license, phatase interactome: An intricate family portrait. FEBS letters 586, 2732–2739 users will need to obtain permission from the license holder to reproduce the material. (2012). To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ 48. Schultze, S. M., Hemmings, B. A., Niessen, M. & Tschopp, O. PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis. Expert reviews in molecular medicine 14, e1 (2012). r The Author(s) 2016

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