Phosphorylation Analysis of Primary Human T Lymphocytes Using

Phosphorylation Analysis of Primary Human T Lymphocytes Using Sequential IMAC and Titanium Oxide Enrichment Montserrat Carrascal,* David Ovelleiro, Vanessa Casas, Marina Gay, and Joaquin Abian CSIC/UAB Proteomics Laboratory, IIBB-CSIC, IDIBAPS, Facultad de Medicina, Campus UAB, 08193 Bellaterra, Spain Received July 7, 2008

T lymphocytes mediate cellular and humoral defense against foreign bodies or autoantigens. An understanding of T-cell information processing furthers studies of the immunological response. We describe a large-scale phosphorylation analysis of primary T cells using a multidimensional separation strategy, involving preparative SDS-PAGE for prefractionation, in-gel digestion and sequential phosphopeptide enrichment using IMAC and TiO2. A total of 281 phosphorylation sites (197 of high confidence, Ascore > 15), mapping to 204 human gene sequences, were identified by LC-MSn analysis in an LTQ linear ion trap. Subsequently, we created the LymPHOS database (http://lymphos.org), which links mass spectrometric peptide information to phosphorylation sites and phosphoprotein sequences. Keywords: Phosphoproteomics • T lymphocytes • immune response • IMAC • titanium oxide • LC-MSn • LymPHOS database

Introduction Protein phosphorylation-dephosphorylation events play a primordial role in cell functions. They are involved in signal transduction, the cell cycle, and apoptosis. A complex network of kinases and phosphatases modulates many aspects of cell activity, including metabolism, motility, growth, and proliferation, through the selective phosphorylation or dephosphorylation of cell proteins.1 Phosphorylation may induce conformational changes that allow active sites to be exposed, thus modulating protein enzymatic activity. It may be the first step in recruiting proteins for functional protein complexes. Also, it may direct the cellular localization of functional protein complexes or alter their degradation rate. Some estimates indicate that about 2-5% of the human genome codifies for kinases and phosphatases.2 Altogether, protein phosphorylation is one of the most frequent posttranslational modification (PTM) in eukaryotic cells, where 30-50% of the expressed proteins may be phosphorylated at some time.3 There are nine known phosphorylation targets: Tyr, Ser, Thr, His, Asp, Glu and, less frequently, Lys, Arg, and Cys. However, in eukaryotic cells, phosphorylation is produced in Ser, Thr, and Tyr, with high differences in relative abundance (1800:200:1 respectively in vertebrates).2 Phosphorylation is a reversible process. The phosphorylation status is determined by the activity of kinases and phosphatases that act in a highly regulated balance. Disturbance of this balance is the basis of diverse pathologies.4,5 Phosphorylation processes are very fast, allowing complex cellular changes to take place in a few seconds or minutes.6,7 For example, the Linker of Activated T cells (LAT) protein, a T lymphocyte adapter protein that couples the T-cell receptor (TCR) to * Towhomcorrespondenceshouldbeaddressed.E-mail:montserrat.carrascal@ gmail.com. 10.1021/pr800500r CCC: $40.75

 2008 American Chemical Society

downstream signaling pathways, is phosphorylated within seconds after TCR stimulation. LAT phosphorylation activates several downstream signaling pathways, which lead to gene regulation and proliferation responses. These responses include dramatic changes in T-cell cytoskeleton, which can be observed within 2 min after activation.7 Some proteins can remain activated for several hours, thus contributing to the long-term cellular response.8 In other cases, proteins have to be rapidly deactivated to maintain the cell response capacity through the corresponding signaling pathway. These changes may involve only a small population of the total number of copies of a cell protein, making its detection by physicochemical procedures such as mass spectrometry one of the most important challenges in Proteomics. The complexity of the phosphorylation process is further increased by the fact that a protein can hold different phosphorylation sites, which may be the target for one or several different kinases/phosphatases. This is a common mechanism that increases the regulatory efficiency of proteins, allowing the same protein to control different activities and different signals to be integrated in the cell.9,10 Global phosphoproteome analysis is thus hampered by the low relative concentration of its components and the dynamic nature of the phosphorylation process. In consequence, only a small fraction of the possible modification sites of proteins have been described to date.11-13 In recent years, several technical advances, including the availability of increasingly sensitive mass spectrometers with better resolution (e.g., orbitraps and FT-ICRs) and new fragmentation methods (e.g., electron transfer dissociation, ETD), have contributed to making this task easier. 14,15 Still, the purification of phosphoproteins and phosphopeptides before mass spectrometric analysis is a mandatory step in this process. Enrichment of phosphopeptides from proteome digests can be carried out using strong cation exchange (SCX) chromatogJournal of Proteome Research 2008, 7, 5167–5176 5167 Published on Web 11/06/2008

research articles raphy, as acidic phosphorylated peptides concentrate in the earlier fractions of the separation.16 Other common procedures are based on the use of metal affinity columns17 or TiO2 phases.18 The original NTA- or IDA-silica absorbents have been superseded by new chelant supports, based on nitriloacetic acid analogs of higher performance and specificity. These new supports do not require the esterification procedures that were needed to eliminate the highly acidic peptides retained in the old supports. Using SCX fractionation, together with a PhosSelect Iron Affinity gel support, an NTA analog from Sigma and shotgun proteomics techniques, Gygi’s group characterized more than 5000 phosphorylation sites in 10 mg of mouse liver.16 In addition, several methods based on chemical reactions have been used to capture the phosphopeptides.19,20In some cases, these reactions led to the removal of the phosphate group in Ser and Thr, allowing spectrometric analysis to be performed without the problems derived from the presence of this group. Bodenmiller et al. used the PAC method (phosphoramidate chemistry) to characterize 535 p-sites in Drosophila, thus showing that this method complements the TiO2 and IMAC procedures.21 In another study, these 3 purification methods were used in parallel, together with high resolution mass spectrometers (FT-ICR). As a result, 10 000 high confidence p-sites were characterized in Drosophila melanogaster.22 However, the complexity of this procedure and the generation of some interference can make it difficult to implement in many laboratories. The largest available collection of human p-sites probably corresponds to the HeLa phosphoproteome reported by Mann’s group. This collection contains over 6000 experimental p-sites and is included in the Phosida database.11 Other protein databases include human phosphorylation information, such as PhosphoEML, with 16 500 p-sites in different eukaryotic species;23,24 Phosphosite, with 70 000 p-sites described in vertebrates;25 and HPRD, with 2000 p-sites in human proteins.26 The main general databases contain annotations of PTMs, including empirical data extracted from the literature and potential phosphorylation sites. In Swiss-Prot/TrEMBL, 4711 phosphoproteins were annotated in December 2007 from a total of 76 123 human proteins. In this study, we examined the human T lymphocyte phosphoproteome, which is a model for the study of signal transduction and altered immune responses. The aim was to characterize new phosphorylation substrates in T lymphocytes. In addition, global phosphorylation profiles in these and other cells may help characterize biological markers of cell status, which could be used as prognostic or diagnostic markers for hypersensitivity or immunodepression-related diseases. TCR activation involves the arrangement of several membrane receptor components (CD3-receptor complex and CD4 or CD8 coreceptors) and the activation of receptor-associated protein tyrosine kinases (PTKs). The activation of these kinases, which are modulated by other phosphatases or kinases like CD45 or Csk, promotes recruitment around the receptor of other intracellular signaling molecules.27 These molecules are responsible for amplifying and transmitting the signals that cause transcriptional activation and cytoskeleton reorganization. The key points in all these processes are the activation of the signaling pathways of the Ras and Rho GTPase families and the metabolism of inositol phospholipids that regulate the intracellular calcium levels and the activity of some serine threonine kinases (STKs). Overall, phosphorylation/dephosphorylation processes play a major role in T lymphocyte 5168

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Carrascal et al. activation. Characterizing the substrates and enzymes involved is the first step toward understanding cell activity during the immune response. Although a few studies have analyzed lymphocyte phosphoproteome, all of them used the well-defined Jurkat T cell line. They studied changes in the phosphotyrosine patterns during T cell activation with different agents like pervanadate,20 Gleevec,28,29 antiCD · and antiCD8,30 or INF-R.31 Most of them used phosphotyrosine immunoprecipitation to enrich the sample in tyrosine phosphorylated proteins or peptides prior to IMAC phosphopeptide purification and LC-MS/MS for sequence characterization. In this study, we focused on the whole set of phosphorylated proteins from human primary circulating T lymphocytes. We characterized 281 phosphorylation sites in a total of 253 phosphopeptide sequences, which mapped to 204 different human gene sequences. To our knowledge, this collection represents the first large-scale phosphoproteomics study in human primary T cells and it is included in an open access database (LymPHOS).

Material and Methods Lymphocyte Isolation and Protein Extraction. Buffy coats were obtained from the Blood Bank of Hospital Clinic and Hospital Vall d’Hebron (Barcelona, Spain). Mononuclear cells (PBMCs, i.e., monocytes and lymphocytes) and platelets were isolated by a Ficoll-Paque (GE, Uppsala, Sweden) gradient centrifugation following standard procedures. This cellular fraction was resuspended in PBS and washed twice (1000× g, 10 min). Platelets were removed by a third centrifugation at 100× g for 10 min. The PBMCs pellet was resuspended in PBS at 108 cell/mL. Adherent cells (monocytes) were removed by 30 min in culture at 37 °C. Then, lymphocytes were recovered in the supernatant, washed twice in PBS, and stored dry at -80 °C. To obtain the protein extract, lymphocytes were lysed with ice-cold 6 M urea, 50 mM Tris pH 7.4, 2.5 mM DTT, phosphatase, and protease inhibitor cocktails. The extract was sonicated 5 times for 5 s (Sonic Vibracell TM) and centrifuged for 30 min at 20 000× g. Proteins in the supernatant were reduced at 56 °C for 30 min and alkylated with 7 mM iodoacetamide (20 °C, 30 min). They were then precipitated with 10% TCA (1/1 v/v), and the pellet was washed with cold acetone and left to dry.32 The protein concentration was measured using an RcDc quantification kit (BioRad, Hercules, CA). SDS-PAGE and In-Gel Digestion. The protein pellet was redissolved in sample buffer (60 mM Tris-HCl pH 6.8, 20% glycerol, 2% SDS, β-mercaptoethanol, bromophenol blue 0.05%), boiled for 5 min, and separated in a hand-poured 10% acrylamide SDS-PAGE gel (MiniProtean, BioRad). The gel was stained with Coomassie Blue, and the complete lane was cut manually into 11 slices that were in-gel digested with trypsin (Promega, Madison, WI), using a Digest MSPro (Intavis, Koeln, Germany) and following standard procedures. Briefly, gel slices were washed with water and 20 mM ammonium bicarbonate pH 7.8, reduced with 10 mM DTT, alkylated with 55 mM iodoacetamide, and digested with trypsin for 16 h at 37 °C. Tryptic peptides were extracted with acetonitrile/water 0.25% TFA. Extracts were evaporated to dryness in a SpeedVac centrifugal vacuum concentrator (Thermo Electron) and redissolved in 200 µL of 250 mM AcOH/30% acetonitrile. Phosphopeptide Enrichment Using IMAC and TiO2. IMAC resin (Phos-Select iron affinity gel, Sigma, St.Louis, MO) was

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Phosphorylation Analysis of Primary Human T Lymphocytes washed three times with 250 mM AcOH/30% acetonitrile and 20 µL was added to each peptide extract. The mixtures were incubated for 90 min at 20 °C with end-to-end rotation in a 500 µL-eppendorf tube. Then, samples were transferred to a Mobicol holder (MoBiTec, Germany) and washed three times with 200 µL of 250 mM AcOH/30% acetonitrile. Phosphopeptides were eluted with 150 µL of 0.5% NH4OH into a 1.5 mL eppendorf containing 20 µL of 10% formic acid. The nonretained fraction was concentrated in the SpeedVac to approximately 10 µL. It was then diluted 5 times with 1 M glycolic acid, 5% TFA, 80% acetonitrile and loaded into a TiO2 minicolumn. This column was prepared in a GelLoader Tip, following a procedure adapted from Larsen and cols.33 Briefly, TiO2 (Titansphere, GL Sciences, Japan) slurry was prepared in acetonitrile, introduced into the GelLoader tip to obtain a support height of 1-2 mm and washed twice with 1 M glycolic acid. Samples were loaded into the tip with a syringe. They were consecutively washed with 10 µL of 1 M glycolic acid, 5% TFA, 80% acetonitrile, 20 µL of 80% acetonitrile, 1% TFA and, finally, 5 µL of water. Phosphopeptides were eluted from the tip with 20 µL of 0.5% NH4OH followed by 1 µL of 30% acetonitrile. The eluate was acidified with 2 µL of formic acid and stored at -80 °C until analysis. LC-MSn Analysis. All the fractions were analyzed by LCMSn using a linear LTQ ion trap equipped with a microESI ion source (ThermoFisher, San Jose, CA). Each extract was concentrated to about 5 µL and diluted to 40 µL with 1% formic acid. The HPLC system was composed of an Agilent 1200 capillary pump, a binary pump, a thermostatted microinjector and a micro switch valve. Separation was carried out using a C18 preconcentration cartridge (Agilent Technologies, Barcelona, Spain) connected to a 10 cm long 150 µm i.d. Vydac C18 column (Vydac, IL). Separation was done at 1 µL/min using a linear acetonitrile gradient from 0 to 40% in 60 min (solvent A: 0.1% formic acid, solvent B: acetonitrile 0.1% formic acid). The LTQ instrument was operated in the positive ion mode with a spray voltage of 2 kV. The scan range of each full MS was m/z 400-2000. The spectrometric analysis was performed in an automatic dependent mode. A full scan followed of 8 MS/MS for the most abundant signals were acquired. A subsequent MS3 scan was performed when a neutral loss of -98, -49, or -32.7 (loss of H3PO4 for the +1, +2, and +3 charged ions, respectively) was detected among the 10 most intense ions. Dynamic exclusion was set to 1 with a time window of 5 min, to minimize the redundant selection of precursor ions. Database Search and Phosphopeptide Validation. MS2 and MS3 fragmentation spectra were searched using SEQUEST (Bioworks v3.3, ThermoFisher, San Jose, CA) against a combined target/decoy database consisting of the Human UniProt (Swiss-Prot + Trembl) database, to which its reversed copy was appended. Search parameters were: peptide mass tolerance, 2 Da; fragment tolerance, 0.8 Da; the enzyme was set to trypsin, allowing up to two missed cleavages; static modification, involving carbamidomethylated cysteine (+57 Da); dynamic modifications, involving methionine oxidation (+16 Da), phosphorylation on serine, threonine and tyrosine (+80 Da) and loss of water from serine and threonine due to the β-elimination of phosphoric acid from the corresponding phophoaminoacid. The SEQUEST search was performed, with a maximum of +3 charges for the precursor ion. Correct peptide sequence identifications were evaluated using both the Xcorr score34 from SEQUEST and the D value,

35

which was calculated as described. A plot of Xcorr versus D values for all matched spectra was prepared. Identifications with scores situated in the area defining a false discovery rate (FDR) < 1% were considered correct. The boundaries of this area were determined with a recursive algorithm that calculated the line Xcorr ) a*D + b and provided a maximum of matches in the target database for this FDR value. To evaluate the confidence of the p-site assignation, an Ascore analysis was performed using a custom Perl script and implementing the algorithm described by Beausoleil et al.36 The Ascore measures the probability of a correct phosphorylation site assignment based on the presence and intensity of fragment ions that are exclusive to a specific site location in the MSn spectra. The original Ascore calculation was modified to take into account the dehydroalanine and dehydrobutyric amino acids, so that it could also be applied to MS3 spectra. In the matching between theoretical and experimental spectra, we used +1 and +2 y and b ion series (for +2 and +3 charged peptides). LymPHOS Database Structure and Design. All the information produced in this study (phosphorylated peptides, localization of these sites within proteins, spectral data in both graphical and text formats), as well as the experimental conditions, have been stored in a web application named LymPHOS (www.lymphos.org). The Xcalibur Development Kit (XDK, Thermo Fisher), a set of programmed COM objects that enables access and manipulation of binary format data, was used to obtain and store the spectrometric data from the raw file. This kit made it possible to extract the spectra of interest from raw files. It uses automatic filtering methods and stores data in text format for subsequent inclusion in the database. Briefly, we analyzed the mass spectrometric data with Bioworks 3.3. The results were evaluated according to the criteria described in the materials and methods. They were assessed in automatic mode by means of VBA-Excel applications and Perl scripts. Each high confidence identification was associated with its corresponding scoring data set (Xcorr, DeltaMass, ∆Cn, etc.), the proteins containing the identified sequence and the mass spectra (a mass-intensity matrix). All this information was stored in a series of text files and uploaded to the web application, where the information was stored using a MySQL relational database management system (www.mysql.org/) (Figure 1). The web application was created using HTML, Perl (www. perl.org) and PHP (www.php.net). The application allows users to search for sequences and sequence tags and provides a graphical visualization of the spectra (through the GD PHP module, www.boutell.com/gd) with an indication of the expected fragments. Researchers can add new entries and annotations from new experiments by means of a simple webform that allows the uploading of the corresponding data files onto the server and makes the new data directly available for consultation (see Supporting Information, Figure 2).

Results and Discusion Four milligrams of protein extracts from purified T cells were separated by SDS-PAGE and the entire gel was excised into 11 slices and digested in-gel with trypsin. For each peptide extract, phosphopeptides were isolated using IMAC purification. To increase phosphopeptide coverage, the nonretained IMAC fraction was submitted to a second purification on TiO2 (Figure 2). Journal of Proteome Research • Vol. 7, No. 12, 2008 5169

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Figure 1. LymPHOS Database structure.

Figure 2. Purified T cell protein lysates were separated by SDSPAGE. The entire gel was excised into 11 slices and digested ingel with trypsin. Phosphopeptides were isolated using sequential IMAC and TiO2 purifications. IMAC and TiO2 eluted fractions were subjected to LC-µESI-ITMSn using a LTQ linear ion trap mass spectrometer.

Both IMAC and TiO2 purified fractions from each gel slice were subjected to LC-µESI-ITMSn, using a LTQ linear ion trap mass spectrometer. Approximately 25 000 MS/MS spectra were acquired from a full set of 22 LC-MSn. The generated data was searched against a protein target/decoy human database, using the sequence database search tool SEQUEST.34 The decoy database was constructed by reversing the sequence of the proteins in the human Swiss-Prot and Trembl databases. The target/decoy strategy is based on the principle that incorrect matches have an equal probability of being derived from either the target or the decoy database.37,38In contrast, spectra with 5170

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Figure 3. D value versus Xcorr. (A) Matches identified in the target and (B) decoy databases. Yellow spots correspond to peptides identified from two consecutive MS2/MS3 scans.

relevant sequence information will match their corresponding sequences in the target database, with higher scores (Figure 3). During the analysis cycle, the MS3 scan ion was only triggered when a phosphate neutral loss was observed from the parent ion in the MS2 spectra. Loss of the phosphate group from either phosphoserine or phosphothreonine39 is very common in ion trap CID spectra. Frequently, this fragmentation carries most of the total ion current, diminishing the sequence information derived from other peptide fragments. This makes a MS3 analysis on the M-phosphate ion necessary. In our data set, 72% of the spectra showed a prominent neutral loss. Thus, a subsequent MS3 scan was performed, using the

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Phosphorylation Analysis of Primary Human T Lymphocytes

Table 1. Summary of the Reported T-Cell Phosphopeptide Set and Its Distribution between the IMAC and TiO2 Fractionsa total

Spectra identified Spectra identified as nonphosphorylated peptides Spectra identified as phosphopeptides Nonredundant phosphopeptides Nonredundant nonphosphorylated peptides Specificityb (%) Total p-sites High confidence p-sitesc pSer:pThr:pTyr ratiod Phosphoproteins Genes

TiO2

IMAC

1639 671

1215 548

424 123

965 252 323

665 197 289

300 84 66

44

41

56

281 202 88:10:2 590 204

a Except where indicated, figures in the table correspond to the number of components in each class. See Supporting Information (Table b 1). Relation between the number of phosphopeptides and the total number of identified sequences. c p-sites with Ascore > 15. d For high confidence p-sites.

2

3

Figure 4. MS and MS spectra obtained in the data-dependent neutral loss analysis. Loss of the phosphate group from either phosphoserine or phosphotreonine was frequently observed in the ion trap CID spectra. In most cases, this fragmentation carries most of the total ion current making a MS3 analysis on the M-phosphate ion necessary.

analysis on the MHn+n, H3PO4 ion (loss of phosphoric acid from the protonated molecular ion) (Figure 4). Despite the dominant neutral loss, most of these spectra (42%) contained ions derived from backbone fragmentation, which allowed cross-validation of the tentative identification with the corresponding MS3 spectra. When MS2 and MS3 spectra led to the identification by SEQUEST of the same phosphopeptide and phosphosite, the identification was considered positive,40-42 regardless of the absolute scores obtained. A final data set of 965 phosphopeptides with a FDR < 1 was obtained using Xcorr and D values as the filtering criteria. The D value score uses a Bayesian statistical algorithm that yields a probability value from SEQUEST scores. Although the D value already includes Xcorr as an important parameter, we found that the use of Xcorr/D value plots were more convenient for the analysis of our phosphopeptide data set than the D value alone. This is probably due to the fact that a number of spectra with different possible phosphorylation sites frequently produce different p-site assignations of similar score, giving rise to low values of ∆Cn, the SEQUEST parameter that measures the score difference between the first and second candidates. As ∆Cn is another important parameter in D value calculation, this effect leads to an underestimation of these assignations. This bias could be partially corrected by including in the data set assignations that were of high Xcorr (Figure 3), despite showing a D value lower than the critical value calculated from the distribution. Out of the total set of phosphopeptides (Supporting Information, Table S1), 28% could be identified only from their MS2 spectrum, 30% from MS3 and 42% were identified independently from both scans. As indicated above, coincident consecutive assignations from MS2 and MS3 were considered as positive, regardless of the absolute score values. Among the

sequences in this last group, only 5 (3 in the IMAC extract and 2 in the TiO2 extract out of a total of 372) matched the decoy database. A manual analysis of the corresponding spectra showed that all of them were of high quality. In fact, two of the MS2/MS3 pairs of spectra could be easily sequenced by hand. Erroneous SEQUEST matches were probably due to the presence of dominant dephosphorylated fragments in the MS3 spectra. The presence of several serine, threonine and/or tyrosine residues in a phosphopeptide sequence often made it difficult to unambiguously characterize the phosphosite from the data in the fragmentation spectra. To resolve this ambiguity, the Ascore algorithm was applied.36 This algorithm evaluates the presence of phosphorylation-derived fragment ions in the spectra. In the original study, it was described that over 99% of phosphopeptide assignation with an Ascore g19 corresponds to a correct p-site assignation.36 In addition, Ascores in the range 15-19 ensure more than 90% of true positives. Sixty nine percent of the spectra in our data set produced assignations with an Ascore g 19. Six percent had Ascores between 15 and 19. The Ascore value for each assigned p-site is included in the Supporting Information (Table 1). All the spectra with Ascore g15 can be found in the LymPHOS database (www. lymphos.org). IMAC and TiO2 Phosphopeptide Enrichment. IMAC phosphopeptide purification is based on phosphate affinity for metals, while TiO2 purification is based on acid-base interactions. These two methods thus show different selectivity, providing different sets of phosphopeptides. These strategies are complementary; as they provide a more complete view of the phosphoproteome components.13,21 Recently, WilsonGrady et al. used parallel IMAC and TiO2 enrichments to isolate the fission yeast phosphoproteome. They found, on average, only 29% of overlap between the two phosphopeptide sets.43 In many cases, however, the amount of sample available makes it difficult to use strategies based on parallel analysis using several isolation methods. Larsen and cols. showed that the application of IMAC fractions to TiO2 columns is a convenient and efficient method in this case. These authors found that 37% more sequences could be obtained using this sequential purification, compared to a unique TiO2 purification. In the Journal of Proteome Research • Vol. 7, No. 12, 2008 5171

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Figure 5. Comparison between amino acid frequencies in the human UniProt database (black) and the phosphopeptide data set identified with TiO2 (white) and IMAC (gray).

Figure 6. Phosphorylation motifs. (A) Motif-X analysis, (B) frequency in our phosphopeptide set of nine general motif groups (prolinedirected, basophilic 1 and 2, CK1 and 2, 14-3-3A and B, ERK1/2 and CDK1/2/5, and (C) frequency in our phosphopeptide set of the phosphoserine/threonine kinase substrate motifs described by Amanchy et al.51

present work, we followed this strategy by applying the nonretained IMAC fraction to the TiO2 column and analyzing both the eluted IMAC and TiO2 fractions. In preliminary experiments, direct purification of full protein extracts by the methods described above led to the identification of only a small number of phosphopeptides (68 phosphopeptides from 1.5 mg of total sample, data not shown) and lower relative recovery levels. Fractionation of the samples was required to achieve efficient phosphopeptide recovery in the subsequent purification steps with IMAC and TiO2. Several studies have reported direct p-analysis from crude extracts using low amounts (µg-mg) of starting material. However, 5172

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most laboratories use SCX chromatography,44 gel electrophoresis or gel-free electrophoresis,45 and IMAC based phosphoprotein purification,46 rather than IMAC or TiO2 purification. In our study, SDS-PAGE was a convenient method for the purpose, as it efficiently eliminates small molecular weight ionic interferences and the protein digest can be directly deposited on the IMAC columns without previous desalting. Desalting is a common requirement when, for example, SCX chromatography is used. A total of 84 and 66 nonredundant phospho and nonphosphopeptides were characterized in the IMAC eluate. The nonretained fractions were then submitted to a TiO2 enrich-

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Phosphorylation Analysis of Primary Human T Lymphocytes ment, from which 197 and 289 nonredundant phospho and nonphosphopeptides were obtained respectively. Despite the specificity of the IMAC procedure being slightly higher (56 vs 41%, calculated as the relation between the number of phosphopeptides and the total number of peptide sequences), the TiO2 sorbent retained a higher number of phosphorylated sequences, including 29 phosphopeptides that could also be found in the IMAC eluates. This higher efficiency of TiO2 is probably the result of the diminished retention power of the IMAC column, due to the presence in the sample of chemicals remaining from the in-gel digestion step (DTT, IAA, acrylamide). When the same procedure was applied to digested HPLC protein fractions, in which there was no DTT and IAA, the average ratio of phosphopeptides found with these two techniques was 1 with only 11% of common peptides (not shown). Properties of the Identified Phosphopeptides. We found that 178 of the unambiguous p-sites were phosphorylated in serine, 20 in threonine, and only 4 in tyrosine (88, 10, and 2% respectively). This distribution is similar to the figures determined in Hela cells by Olsen et al. (86.4:11,8:1.8)11 and in mouse liver by Ville´n et al. (88:11:1).16 Moreover, 224 of the phosphopeptides were monophosphorylated and only 26 were phosphorylated at two sites. Triply (or higher) phosphorylated peptides were not detected. This may be due in part to ionization suppression by the mono and nonphosphorylated peptides in the ESI source, as well as to the fact that low informative MS3 spectra dominate the neutral loss signals that can be obtained from these phosphopeptides. A method that could prevent this problem was described recently by Thingholm et al. These authors employed sequential basic and acidic elutions of the IMAC columns (SIMAC) to fractionate the mono from the multiply phosphorylated peptides that were recovered in the basic eluate. Multiply phosphorylated peptides were then analyzed using a specific MS3 acquisition that automatically selects and fragments ions originating from a minimum of 2 phosphate groups from the parent ion.47 The observed bias could also be corrected with new fragmentation methods, such as ETD and ECD, in which extensive loss of phosphate groups is not a dominant process.15,48 An analysis of the aminoacid composition of the phosphopeptide set reveals that there were higher proportions of glutamic and aspartic acids and of serine and proline in both enrichment methods, relative to those expected from the tryptic peptides, as calculated from data in the human Uniprot database (Figure 5). Although new TiO2 and IMAC enrichment materials have shown higher selectivity than the classic ones,49 undesirable adsorption of acidic peptides can still be observed. Furthermore, the proportion of acidic aminoacids, glutamic and aspartic, is significatively higher with TiO2 than IMAC (3.2 ( 2.5 vs 4.2 ( 3.3 acidic aminoacids per peptide for IMAC and TiO2 respectively, p < 0.004) even when the sample is loaded in 1 M glycolic acid,33 as in our method. In contrast, the high proportion of Ser in the IMAC fraction reflects the higher specificity of this sorbent for Ser-phosphorylated peptides. The increase in proline can be explained by the high number of peptides containing a phosphorylation in serine with a prolinedirected motif. Phosphopeptide sequences with Ascore > 15 were submitted to the Motif-x algorithm50 to detect kinase-specific motifs. Sequences were either shortened or extended to append 6 aminoacids to the left and right of the phosphorylation site. Extensions were performed using the IPI human database. The occurrence parameter was set to 20 and the significance to

Figure 7. Subcellular location of the phosphoproteins included in our data set. The information was extracted from the Subcellular Location comment from the UniProt database. This annotation was found for 174 of the 590 proteins. Table 2. Panther59 Gene Ontology Analysis of the 204 Genes Mapped by Our Phosphopeptide Dataset (www.pahtherdb.org)a number of genes molecular function

data set expected p-value

Nucleic acid binding Actin binding cytoskeletal protein Cytoskeletal protein mRNA processing factor Chromatin/chromatin-binding protein Ribonucleoprotein Other RNA-binding protein mRNA splicing factor Nonmotor Actin binding protein Unclassified Other (244 categories)

56 17 22 10 9 7 9 7 8 54 264

22.8 2.9 6.6 1.1 1.2 0.8 1.5 0.8 1.3 92.4 252

3.9-9 1.2-6 2.8-5 4.4-5 7.8-4 3.2-3 3.9-3 4.4-3 9.8-3 6.7-7

number of genes biological process

Nucleoside, nucleotide and nucleic acid metabolism Pre-mRNA processing Cell cycle mRNA splicing Mitosis Cell structure and motility Cell motility Unclassified Other (234 categories)

data set

expected

p-value

52

25.3

8.3-6

14 22 11 13 22 11 59 395

2.3 7.5 1.7 2.8 8.7 2.7 95.4 374

1.4-5 2.5-4 3.0-4 1.1-3 2.1-3 1.6-2 4.6-6

a Dataset: number of genes assigned to the corresponding function or biological process category. Expected: number of genes expected in a given category on the basis of the distribution in the Panther reference list (human AB1700 genome database). P-value: Panther Binomial probability indicating the significance of the difference between experimental and expected figures.

10-6. The analysis revealed 50 motifs for proline-directed kinases ([pS/pT]P), 38 acidophilic motifs (sD) and 20 basophilic motifs (RxxpS) (Figure 6A). When we analyzed the data set for the presence of 12 different phosphorylation motif categories,10 the most frequently observed groups were those of prolinedirected and casein kinase 2 motifs (Figure 6B). Recently, Amanchy et al. reported an exhaustive list of 170 phosphoserine/threonine kinase substrate motifs.51 Our data set could be organized following this classification into seventy-five different motifs (Figure 6C). Besides CK2 and proline-directed Journal of Proteome Research • Vol. 7, No. 12, 2008 5173

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Figure 8. Workflow for data processing and LymPHOS database loading. A Bioworks report was generated in Excel format. Using the Development Xcalibur Kit (XDK) and Visual Basic for Applications inside Excel, the MS values (MS2 or MS3) were extracted for each scan and the D value was calculated. Data filtering was then performed on the basis of the mass spectra scores and the coincidence of identifications from both MS2/MS3 scans. A custom-made application processed the filtered data set to obtain mass spectral information for each phosphopeptide sequence (through the XDK), as well as the sequence of every possible protein containing it. This application also calculated the Ascore value to discriminate between p-site candidates when several phosphorylation sites were possible. The resulting output file contains all the information related to each validated peptide: scan number, mass, charge, Bioworks and SEQUEST scores, retention time, MSn state, D value, Ascore, mass-intensity matrix, and putative proteins (including AC number, name, description and sequence). This output file is generated in a format that can be directly uploaded into the LymPHOS web application for database storage.

motifs, a high number of p-sites were found in PKC and PKA kinase motifs. Phosphoproteins and p-Sites. Each phosphopeptide was searched against the Uniprot database (release of April 4, 2008). The full set of phosphopeptides pointed to a total of 590 tentative source proteins that defined 204 genes. On average, each phosphopeptide matched 3 different entries in the database, with a maximum for a 30 aa pTyr, pSer diphosphopeptide, pointing to 17 Actin light chain isoforms. Only 28% of the sequences pointed to only one database sequence. The subcellular location data for the proteins in our data set for which this information was available in Uniprot (174 from 590 proteins) cover many categories, including cytoplasmic, organelle and membrane proteins (Figure 7). The most abundant group, however, were nuclear proteins. Fifty-nine percent of the proteins were found annotated with a nuclear location in the Uniprot database. A gene ontology analysis of the data set with the panther classification system (http://pantherdb.org) showed that 36% of the genes with annotated biological process in this datababase (150 from the set of 204 genes) were involved in nucleoside, nucleotide and nucleic acid metabolism. Compared with the expected values from the ABI1700 human database, our data set shows an important, significant enrichment of this group of genes (Table 2). An analysis of the molecular functions in which these genes are involved, showed a similar enrichment (37%) for the group of genes involved in nucleic acid binding. Nearly 40% of our reported p-sites were not described in the in vivo experiments in the Phosphosite database (http:// www.phosphosite.org), the most comprehensive database of phosphorylation data. However, 30% of our p-sites were recorded in the database, due to similarity with known phosphoproteins or because of the existence of unpublished MS/ 5174

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MS spectra. Out of the p-sites found experimentally, only 16 were described in Jurkat T cells. Only 1 p-site, also found in Jurkat, was reported from the primary T cells in the database. Thirty of the nonannotated p-sites corresponded to known phosphoproteins, while 20 p-sites where found in proteins that were not described in Phosphosite. Although a high number of p-sites could be unambiguously characterized from the different T lymphocyte extracts in our experiments, the absolute number of identifications was low when they are compared to some other large-scale studies in other tissues.11,16,22 As the most limiting step in the workpath was related to the uncertainty in sequence identification, the number of phosphorylated peptides identified would probably be higher using high-resolution mass spectrometry.14 Novel phosphorylation sites on known regulatory proteins could indicate new functions for these molecules and could help in the understanding of T cell processes during the immune response. In our data set, we describe a new p-site (Ser1685) in the dedicator of cytokines protein 2 (DOCK2), a hematopoietic cell-specific protein that is essential for lymphocyte migration.52 Previous studies in Jurkat T cells, using antiphosphotyrosine immunoprecipitation and mass spectrometry, described phosphorylation in two tyrosines.28,53 This protein is involved in the T receptor signaling pathway, where it operates as a guanine-nucleotide exchange factor, activating two small GTPases. It is also involved in Rac activation54 and is required for efficient adhesion.55 Another p-site (Ser475) was also described for Rho GTPase-activating protein 9, a GTPase activator for the Rho-type GTPases that regulates adhesion in these cells.56 Other new p-sites were described in proteins known to be preferentially expressed in lymphocytes. Two new p-sites were described for lymphocyte-specific protein 1, a know phosphop-

Phosphorylation Analysis of Primary Human T Lymphocytes rotein that participates in the regulation of neutrophil motility and in transendothelial migration.57 In addition, 4 new modified positions were found for the bridging integrator 2 (BIN2), a member of the BAR adaptor gene family that could be involved in tumor suppression.58 LymPHOS Database: a Phosphopeptide Database in Human Peripheral T-Cells. To organize the phosphopeptide data, we built an open access relational database called LymPHOS that currently includes phosphopeptide sequences, p-sites, and information about the proteins that contain these phosphopeptides (http://www.lymphos.org). This information is linked to the experimental data including mass spectra, which are presented graphically and can be downloaded. The web page provides tools for sequence searches either by protein name or by peptide tag sequences. Search results consist of a list of matching peptides or proteins (See Supporting Information, Figure S1). In peptide searches, each peptide match is linked to the corresponding MSn spectra, SEQUEST assignation scores (Xcorr, ∆CN, D value), and all the proteins to which the sequence is pointing. In protein searches, results include the protein sequence, the phosphopeptides that define this sequence, and a link to the Uniprot database (Figure 7).

Conclusions SDS-PAGE fractionation, followed by in-gel digestion and sequential enrichment of the tryptic phosphopeptides by sequential IMAC and TiO2 procedures, allowed the identification of more than 250 phosphorylation sites in peripheral T cells, some of which have not been described previously. The complementarity of both enrichment techniques was also shown. At present, this study is the only phosphoproteomic analysis of primary T cells. This data was organized in an open access database called LymPHOS, where phosphosite information is linked to mass spectrometric information. Several novel lymphocyte specific p-sites are described that could be a source of information for future studies on the role of phosphorylation in T-cell functions. The data from resting T cells presented here will be the basis for further studies involving the comparison of phosphoproteomes from stimulated lymphocytes, using different agonists and conditions.

Acknowledgment. We acknowledge Prof Emilio Gelpı´ (IIBB, CSIC, Barcelona, Spain) and Prof. Concha Gil (UCM, Madrid, Spain) for comments on the original manuscript. This work was supported by grant BIO2004-01788 from the Ministerio de Ciencia y Tecnologı´a. The LP-CSIC/UAB is a member of ProteoRed (http://www.proteored.org), funded by Genoma Spain, and follows the quality criteria set up by ProteoRed standards. Supporting Information Available: Supplementary Table 1 lists all identified phosphopeptides and corresponding analytical data. Supplementary Figure 1 is a screenshot of a LymPHOS web page (Protein View). Supplementary Figure 2 is a screenshot of a LymPHOS web page (Webform for data uploading). This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Hunter, T. Signaling - 2000 and beyond. Cell 2000, 1000, 113–127.

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