From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

2009 114: e10-e19 Prepublished online May 12, 2009; doi:10.1182/blood-2009-02-203828 Platelet membrane proteomics: a novel repository for functional research

Urs Lewandrowski, Stefanie Wortelkamp, Katharina Lohrig, René P. Zahedi, Dirk A. Wolters, Ulrich Walter and Albert Sickmann

Updated information and services can be found at: http://bloodjournal.hematologylibrary.org/content/114/1/e10.full.html Articles on similar topics can be found in the following Blood collections e-Blood (113 articles) Platelets and Thrombopoiesis (407 articles)

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PLATELETS AND THROMBOPOIESIS

e-Blood Platelet membrane proteomics: a novel repository for functional research

Urs Lewandrowski,1 Stefanie Wortelkamp,1 Katharina Lohrig,2 Rene´P. Zahedi,1 Dirk A. Wolters,2 Ulrich Walter,3 and Albert Sickmann1,4

1Institute for Analytical Sciences (ISAS), Dortmund; 2Department of Analytical Chemistry, Ruhr-University-Bochum, Bochum; 3Institute for Clinical Biochemistry and Pathobiochemistry and Central Laboratory, University Wu¨rzburg, Wu¨rzburg; and 4Medizinisches Proteom-Center (MPC), Ruhr-University-Bochum, Bochum, Germany

Being central players in thrombosis and are not accessible to large-scale genomic provides a brief overview of gene ontol- hemostasis, platelets react in manifold screens and thus a comprehensive inven- ogy subcellular and functional classifica- and complex ways to extracellular stimuli. tory of membrane proteins is still miss- tion, as well as interaction network analy- Cell-matrix and cell-cell interactions are ing. For this reason, we here present an sis. In addition, the mass spectrometric mandatory for initial adhesion as well as advanced proteomic setup for the de- data were used to assemble a first tenta- for final development of stable plugs. tailed analysis of enriched platelet plasma tive relative quantification of large-scale Primary interfaces for interactions are membranes and the so far most complete data on the protein level. We therefore plasma membrane proteins, of which collection of platelet membrane proteins. estimate the presented data to be of ma- many have been identified over the past In summary, 1282 proteins were identi- jor interest to the platelet research field decades in individual studies. However, fied, of which more than half are termed and to support rational design of func- due to their enucleate structure, platelets to be of membrane origin. This study tional studies. (Blood. 2009;114:e10-e19) Introduction

Platelets are essential mediators of hemostasis and are well known for brane purifications in combination with suitable separation tech- their major role in thrombotic events. In the context of increasing niques,1 which achieved identification of nearly 300 proteins, of numbers of cardiovascular diseases, platelets are of premier interest for which approximately half were of membrane origin. Of these, G6B scientific research. Owing to their unique enucleate ultrastructure (still was later confirmed by Senis et al to be a novel immunoreceptor including organelles such as Golgi, endoplasmic reticulum, or mitochon- tyrosine-based inhibitory motif protein4 as part of an additional dria), common molecular biology–based methods can hardly be applied. study on the platelet membrane proteome. Still, only 46 plasma Their megakaryocyte-derived maturation process renders platelets incon- membrane components could be observed within a total of venient targets for genome-based research approaches. Although mouse 136 membrane proteins in this study, thereby indicating a potential knockout models were generated for a range of functional studies, they gap of knowledge regarding protein membrane constituents. Plasma are limited mostly to known protein components of the platelet system, membrane proteins are among the key targets for platelet drug and estimated to be of functional importance. Moreover, protein synthesis in functional research, since they are the primary interface to extracel- platelets is limited, and a direct correlation between mRNAprofiling and lular stimuli and therefore also for pharmaceutical treatment of protein presence is problematic at best. Based on these limitations, platelet-related disorders. We therefore sought to increase the modern proteome analysis might be a key asset for the analysis of the identification rate of platelet membrane proteins with a major focus platelet proteome and its so far unknown components. The derived on the plasma membrane. knowledge of these newly identified proteins represents a rich source for For plasma membrane enrichment, several methods have been the rational design of functional studies. established in the past, with density gradients being the most Despite their major functional importance a complete inventory common. In addition, lectins have been used to additionally purify of the platelet proteome is far from being accessible. Due to the plasma membrane glycoproteins.1 However, due to our experience development of biomolecular mass spectrometry, a series of with efficient enrichment of rat brain plasma membranes by medium- to large-scale studies was conducted upon the platelet 2-phase aqueous partitioning systems,5,6 we adopted this technique proteome. Early studies aiming for a complete overview of platelet for platelet membranes. Two-phase partitioning is based on sorting proteins most often used 2-dimensional polyacrylamide gel electro- of vesicles upon their physicochemical surface properties (such as phoresis (2D-PAGE) as primary separation technique, frequently in hydrophilicity/hydrophobicity and net surface charge, possibly due conjunction with peptide mass fingerprint identification of proteins. to their phospholipid composition)7 within a defined 2-polymer However, as demonstrated by Moebius et al1 2D-PAGE–based system. A common system is the polyethyleneglycol (PEG)/ studies of complete platelets2,3 were reasonably unsuccessful in dextran system, where plasma membranes show the highest affinity elucidating membrane components compared with targeted mem- for the more hydrophobic upper PEG phase in comparison with

Submitted February 4, 2009; accepted April 30, 2009. Prepublished online as The publication costs of this article were defrayed in part by page charge Blood First Edition paper, May 12, 2009; DOI 10.1182/blood-2009-02-203828. payment. Therefore, and solely to indicate this fact, this article is hereby marked ‘‘advertisement’’ in accordance with 18 USC section 1734. The online version of this article contains a data supplement. © 2009 by The American Society of Hematology

e10 BLOOD, 2 JULY 2009 ⅐ VOLUME 114, NUMBER 1 From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

BLOOD, 2 JULY 2009 ⅐ VOLUME 114, NUMBER 1 PLATELET MEMBRANE PROTEOME e11

other membrane vesicles.8 We already successfully used this 1D-PAGE and digests technique for analysis of plasma membrane N-glycosylation sites Platelet membrane pellets from 2-phase partitioning systems were reconsti- 9,10 on human platelets. In contrast, the current study intends to tuted in 1 ϫ LDS-sample buffer (Invitrogen). After incubation at 75°C for provide a general overview of membrane proteins within these 15 minutes, samples were applied to 4% to 12% Bis-Tris gels using a preparations to allow for a more global assessment of the mem- MOPS buffer system (NuPAGE-Novex; Invitrogen). Protein separation was brane protein composition. followed by colloidal Coomassie staining.15 Subsequently, gel lanes were Here, 3 major strategies were pursued for protein identification: cut in 1-mm slices and bands were treated as described previously.5 After (1) For a global analysis, separation of proteins by 1-dimensional reduction and alkylation, proteins were tryptically digested in-gel and sodium dodecyl sulfate–polyacrylamide gel electrophoresis (1D- peptides were extracted by 0.1% trifluoroacetic acid. SDS-PAGE) followed by nano–liquid chromatography (LC)– tandem mass spectrometry detection of peptides, basically as COFRADIC 6 described previously was conducted. (2) To address issues com- A detailed description of COFRADIC experimental procedures is given in monly encountered with PAGE separation of membrane proteins, the supplemental methods. highly complex peptide mixtures from membrane proteins were separated by strong cation exchange and reversed-phase chromatog- Mass spectrometry raphy within a MudPIT setup (Multidimensional Protein Identifica- tion Technology) prior to mass spectrometric detection.11 (3) To Nano-LC-tandem mass spectrometry of 1D-PAGE and CO- reduce the high complexity of peptide mixtures prior detection, FRADIC fractions. Tryptic peptides from in-gel digestion of Combined Fractional Diagonal Chromatography (COFRADIC) 1D-PAGE bands were separated by nano-LC and detected by a was used to isolate N-terminal, methionine- or cysteine-containing Qtrap4000 mass spectrometer (Applied Biosystems) as described 9 peptides means chemical derivatization and standardized liquid previously. In turn, mass spectrometric detection of COFRADIC chromatography.12,13 Thus, in combination with aqueous 2-phase fractions on a QStar Elite Q-TOF (Applied Biosystems) was partitioning, this 3-pronged approach enabled a comprehensive preceded by nano-LC separation, essentially as described 18 analysis of the platelet membrane proteome that clearly exceeds previously. our previous knowledge about the plasma membrane inventory. Multidimensional protein identification technology. MudPIT analyses were conducted following a variation of a protocol by Wolters et al.11 A detailed methods section is given in the supplemental methods. Methods Data evaluation Materials Shotgun approaches. Raw data were transformed into Mascot19 Unless stated otherwise, chemicals were purchased from Merck KGaA in generic format using either plug-ins for Analyst 1.4.2 (mascot.dll, analytical quality or better. Water (18 M⍀) was obtained from an Elga MatrixScience) for Qtrap data or ltq dta.exe for LTQ-XL data. Labwater system. The resulting peak lists were searched against a concatenated forward/reversed human subset of the Swiss-Prot database20 (http:// Platelet purification www.expasy.org, 20 834 sequences21) using either Mascot (Matrix- 22 Human platelets were prepared based on procedures described previ- Science) or Omssa as search engines. For Omssa and Mascot, the ously9,14,15 from fresh apheresis concentrates (leukocyte depleted, ϳ 2 ϫ 105 following search parameters were used: Trypsin was chosen as leukocytes, ϳ 6 ϫ 105 erythrocytes, and 2-4 ϫ 1011 platelets/250 mL; protease with one miscleavage site allowed, carbamidomethylation Department of Transfusion Medicine, University Wu¨rzburg) by additional (C) was set as fixed, and oxidation (M) was set as variable differential centrifugation steps to further diminish potential cellular modification. Precursor and tandem mass spectrometry (MS/MS)– contaminations; a detailed procedure is given in the supplemental methods, ion tolerances were limited to 0.05 Da (Qtrap) and 0.01 Da (LTQ), available on the Blood website; see the Supplemental Materials link at the respectively. Subsequent to searches, the result-files of Omssa top of the online article. Use of platelets was approved by the ethics and Mascot were combined in Masssieve (http://www. committee of the University of Wu¨rzburg and donors were informed upon proteomecommons.org/dev/masssieve).23 As filter criteria, P value use of concentrates for research purposes in accordance with the Declara- tion of Helsinki. cutoffs were set to .05 for Mascot and .01 for Omssa. Furthermore, hits were limited to proteins that feature at least 2 different significantly identified peptides. Thereby, a false-positive rate of Plasma membrane enrichment less than 1% was achieved. Platelet plasma membranes were enriched as previously described by COFRADIC. COFRADIC data were searched against a 2-phase partitioning.9,16 Briefly, 2-phase systems consisting of 6.3% human subset of the Swiss-Prot database (see “Shotgun ap- polyethyleneglycol 3350 and dextran T500 (Roth) each in 15 mM Tris, pH proaches”) using Mascot (MatrixScience). Mascot generic files 7.8, were equilibrated overnight at 6°C. For separation of 100 mg (wet were generated using the plug-in to Analyst QS 2.0 (mascot.dll; weight) platelets, a 20-mL system (1:1 vol/vol of the equilibrated PEG and MatrixScience). Although the different approaches necessitated dextran phases) was used. Lysis was accomplished by repetitive ultrasonic individual settings, the following general parameters were used: ϫ bursts (6 15 seconds) and subsequent phase separation was achieved by Both precursor and MS/MS tolerance were set to 0.2 Da, and one centrifugation at 500g for 10 minutes at 6°C. The top PEG phase was missed cleavage was allowed. Depending upon the type of peptide extracted twice with equal volumes of fresh dextran phase. The final upper PEG phase was diluted 1:1 with water, and membranes were pelleted by that was sorted for the Mascot, modification parameters were set ultracentrifugation at 100 000g for 1 hour at 4°C in a TLA 100.4 rotor accordingly as seen in Table 1. All resulting peptide identifications (Beckman Coulter). Removal of cytoplasmic and peripheral membrane were manually validated regarding presence of ion series, dominant proteins was achieved by 2-fold carbonate extraction in 100 mM sodium fragmentation patterns (eg, adjacent to proline), and overall signal carbonate, pH 11.5.17 Pellets were stored at Ϫ80°C until further use. intensity. From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

e12 LEWANDROWSKI et al BLOOD, 2 JULY 2009 ⅐ VOLUME 114, NUMBER 1

Table 1. Mascot search parameters for COFRADIC approaches Fixed modification Variable modification

N-terminal COFRADIC (protease: Arg-C/P) Oxidation to sulphoxide (M), trideutero-acetylation (K), Pyroglutamate (N-term Q), pyrocarbamidomethyl carbamidomethylation (C) cysteine (N-term alkylated C), acetylation (N-term), trideutero-acetlyation (N-term) Met-/Cys-COFRADIC (protease: trypsin) Oxidation to sulphoxide (M), carbamidomethylation (C) Pyroglutamate (N-term Q), pyrocarbamidomethyl cysteine (N-term alkylated C), acetylation (N-term)

Spectral counting. A version of the exponentially modified identified unambiguously (superset, subsumable, equivalent; total of protein abundance index (emPAI)24 approach was used to gain 167 protein features), were discarded for the current analysis. For further quantitative information of protein abundance by spectral counting. use, all data including the latter are presented in a supplemental table Therefore, the number of possible tryptic peptides (Nobservable) was along with identified peptide sequences. calculated for each protein with resulting peptides in the range of m/z 760-4000 Da. emPAI indices were calculated as ln(10ˆ[Nobserved/ Combined fractional diagonal chromatography ϫ observable {5 N }]). The number of observed peptide hits was extracted Using a peptide-centric strategy, 3 approaches aiming for N- from the Masssieve result lists for each protein. Quantification was terminal, and cysteine- and methionine-containing peptides were performed for discrete protein hits from shotgun data only. used, identifying a total of 498 proteins. Methionine- and cysteine- Differentiable hits share peptides with other proteins, whereas the based COFRADIC strategies were able to identify 274 and COFRADIC approach concentrates on subsets of peptides, render- 219 proteins, respectively, whereas N-terminal COFRADIC en- ing both data sets unsuitable for the spectral counting approach. abled 160 protein identifications (Figure 1). The 3 approaches exhibited highly complementary results shown by the 360 proteins, which were covered by only one of Results the approaches each. This stresses the necessity for parallel application of all 3 analysis variants. Although the identification General overview of the proteins is based on individual mass spectra, all peptide By the combined approaches of MudPIT, 1D-PAGE coupled to hits were manually validated to account for correct annotation nano-LC-tandem mass spectrometry, as well as COFRADIC, a and identification by completeness of ion series. In addition, all total of 1282 proteins were identified within the platelet membrane spectra are listed in the pride database for public access preparations derived from aqueous 2-phase partitioning. Thereof, (http://www.ebi.ac.uk/pride/init.do; experiment accession no. 498 proteins were identified with peptide-centric COFRADIC on 8127 [methionine], no. 8128 [N-terminal], and no. 8129 [cys- single-peptide basis, whereas a total of 1202 proteins were teine]).27 COFRADIC enabled protein identification ranging accessible by the combined results of the 2 shotgun-based ap- from high abundant proteins, such as integrin alpha-IIb and proaches. A major intersection of 418 proteins was determined other integrins (integrin alpha-5 or alpha-6), down to low between shotgun and COFRADIC data sets. abundant proteins, such as the G-protein–coupled receptor PAR1. In combination with the 2-phase aqueous partitioning Shotgun data system, COFRADIC enabled the identification of 234 proteins Mass spectra of the 2 shotgun approaches were searched by (47%) with at least one predicted transmembrane domain 28 2 independent search engines, Omssa25 and Mascot.26 Results from (prediction by TMHMM 2.0 ). For comparison, we mapped the 3 MudPIT and 3 independent 1D-PAGE experiments were directly IPI-based accessions of another COFRADIC-based study on 29 30 combined after searches. They showed a large correlation of the whole platelets lysates back to Swiss-Prot accession, yield- engines on the peptide level with 7841 peptides being identified by ing only a share of 17% proteins with at least one TMD (66 of Omssa and 6895 peptides identified by Mascot, with a major 385). Clearly, aqueous 2-phase partitioning-based membrane intersection of 5918 peptides. The parallel use of Mascot and purification caused a nearly 3-fold increase in membrane protein Omssa therefore extended the results by approximately 14% (based identification rate. on Mascot hits) and crosswise confirmed the majority of hits for Although only 80 proteins were exclusively identified by each search engine. After data evaluation of result files in Masssieve, COFRADIC, the approach also yields additional protein-related only protein hits with at least 2 valid peptide identifications were information. In case of N-term COFRADIC, the in vivo existing accepted. This limitation alongside a false-positive discovery rate N-termini are determined, of which a few examples are shown in ranging below 1% enabled presentation of a highly reliable and Figure 1 (for a complete list, compare supplemental table). In accurate data set. Therefore, we also refrained from introducing comparison with the Swiss-Prot database, those N-termini often proteins with only one identified peptide, which would have concur with literature as shown for GpIX, JAM1, or GpIB-beta. In resulted in more than 2000 protein identifications. To further the case of less known proteins, however, the N-termini are so far remove ambiguous data, a differentiation between discrete protein unknown and can now be added to the database information or be hits (934) and differentiable protein hits (268) was made. These used for functional experiments. However, it should be noted that proteins are either identified by discrete peptides that are shared N-terminal processing might also occur during the apheresis used with no other protein sequence, or they are partially identified by for production of the platelet concentrates. shared peptides. However, additional peptides always guarantee the Enrichment of membrane proteins correct annotation of the protein (isoform). Indeed, about three quarters of identifications were based on 3 or more peptides, and the average Being targeted at membrane proteins and more specifically those sequence coverage was 21.8%. Other proteins, which could not be located in the plasma membrane, an analysis of potential TMDs From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

BLOOD, 2 JULY 2009 ⅐ VOLUME 114, NUMBER 1 PLATELET MEMBRANE PROTEOME e13

Figure 1. Combined fractional diagonal chromatography results. (A) Distribution of the 498 proteins identified by COFRADIC with respect to the 3 different approaches. (B) Exem- plary scheme of N-terminal COFRADIC. Upon blockage of free amines (i) and tryptic digest (ii), fractions are collected during a first-dimension high-performance liquid chromatography run (iii). Due to subsequent blockage by TNBS, internal peptides are shifted during a second high-performance liquid chromatography run (iv), whereas true N-terminal peptides are eluted in the same timeframe as before (iii). (C) COFRADIC results are mostly covered as well by identifications based on shotgun approaches. (D) List of selected N-termini elucidated by N-terminal CO- FRADIC from platelet membrane proteins. N-termini often differ from literature (Swiss-Prot database) or are newly assigned.

was conducted using TMHMM 2.0 for the complete data set and in though soluble proteins such as actin (hash key marker) are addition also for several previous studies found in the literature. largely depleted, plasma membrane proteins such as gpIIb/IIIa The results are summarized in the bar diagram of Figure 2A. (asterisks) are heavily enriched. This observation is congruent Clearly, the current study provides a tremendous increase of protein with Moebius et al,1 showing a similar distribution of actin and identifications featuring at least one TMD. Although studies using gpIIb/IIIa, although with a higher share of actin remaining. This, whole platelet lysates (O’Neill/Garcia/Guerrier) detected 63 pro- however, is attributed to the additional, second carbonate teins in this case, the current study features a more than 4-fold extraction step17 in the current study, obviously removing larger increase. In addition, in comparison with more focused studies on parts of the membrane cytoskeleton. plasma membranes or microparticles, the current analysis encom- The subcellular distribution of proteins was estimated by passes a far superior number of multimembrane-pass proteins, for their GO annotations.31 Therefore, Ontologizer 2.032 was used to example, 30 proteins with 7 transmembrane domains including sort proteins as shown by Figure 3. Evidently, a high number of several G-protein–coupled receptors such as PAR1, PAR4, or the plasma membrane components (371) are present within the potential G-protein–coupled receptor 92. Nearly a hundred proteins 1282 protein data set, including 142 integral plasma membrane were predicted with equal or more than 8 TMDs including components. Furthermore, a range of proteins from other adenylate cyclases 3, 5, and 6, proteins of the solute carrier families membrane-bound compartments, such as endoplasmic reticulum 12, 22, 23, and 40, as well as many less studied proteins such as the (ER) (199), Golgi apparatus (148), and vesicles (140), has been 2-pore calcium channel protein 1. In conclusion, TMHMM 2.0 identified. A comparison of accessions between summed ER, analyses confirmed the improved discovery rate of membrane Golgi, and vesicle (388) and plasma membrane proteins (371) proteins by determining 626 membrane-spanning proteins. yields a major intersection of 116 proteins. Obviously those To enable a more direct impression of the purification proteins have a certain distribution or are shuttling between the success, Figure 2B depicts the Coomassie G250–stained SDS- mentioned organelles. However, a major share of the proteins PAGE of whole platelet lysates in comparison with the purified has no determined subcellular localization at all, judged by their membranes derived from aqueous 2-phase partitioning. Al- GO terms, and represents protein species not yet characterized.

Figure 2. TMHMM-based interstudy comparison and membrane protein enrichment. (A) Prediction of mem- brane spanning proteins was done by TMHMM 2.0; comparison with several published studies on the platelet proteome clearly shows an increased number of identi- fied membrane proteins by the current approach. (B) CBB-G250–stained 1D-SDS-PAGE of whole platelet lysates and enriched plasma membranes by aqueous 2-phase partitioning (* indicates gpIIb/IIIa; #, actin; pro- teins were identified by mass spectrometric sequencing). From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

e14 LEWANDROWSKI et al BLOOD, 2 JULY 2009 ⅐ VOLUME 114, NUMBER 1

classes, we used GO annotations to highlight protein classes of potential interest. Being the primary interface for extracellular stimuli, the platelet plasma membrane is supposed to contain a large selection of proteins involved in signal transduction. Indeed, 290 proteins were returned by this entry via their GO terms, including 156 proteins with signal transduction activity and encompassing 104 entries with receptor activity. Despite this high number, it has to be noted that many other activities may not be reflected by GO terms, yet. Although it is far beyond the scope of this study to discuss all membrane proteins in total, some examples of membrane-residing protein classes will be briefly shown: G-protein–coupled receptors Figure 3. Gene ontology (GO)–based subcellular annotation of identified (GPCRs), proteins of cellular adhesion, and membrane ordering proteins. Clearly, high numbers of membrane-associated proteins (882) were identified with a major share of 371 belonging directly to the plasma membrane proteins. Furthermore, a complete list of proteins sorted by GO compartment. terms into (functional) classes is presented within the supplemental table or may rapidly be assembled using Ontologizer (Table 2). In a recent study, Amisten et al44 probed for GPCR-derived mRNA in platelet transcripts, identifying 28 GPCRs and quantify- Functional characterization ing 12 receptors on the transcript level. In our current data set, A complete functional characterization of the current protein 13 GPCRs are either GO annotated (AVPR1A, CCR4, CD97, survey seems improbable, especially due to the diverse functions of CXCR4, LPAR5/GPR92, P2RY1, P2RY12, PTAFR, PTGDR, identified proteins. To provide some estimate of functional sub- PTGIR, XPR1) or otherwise known GPCRs (PAR1, PAR4). Only

Table 2. Algorithms and tools suitable for analysis of platelet proteome data Name Description Source

UniProt Curated database with linked information sources, http://www.uniprot.org/33 retrieval of fasta sequences, etc Protein identifier cross reference service (PICR) Cross-reference algorithm for accession exchange http://www.ebi.ac.uk/Tools/picr/34 between different formats (eg, NCBI vs Swiss-Prot or gene name vs Swiss-Prot accession) Expasy Proteomics server for various prediction and http://www.expasy.org/tools/ sequence analysis programs (eg, prediction of phosphorylation and glycosylation sites, TMDs, import sequences, domains) TMHMM 2.0 Prediction of transmembrane domains http://www.cbs.dtu.dk/services/TMHMM/ Simple Modular Architechture Research Tool Prediction and annotation of protein domains, http://smart.embl-heidelberg.de/smart/batch.pl35 (SMART) internal links to PFAM ProtFun 2.2 Ab initio prediction of protein function, enzymatic http://www.cbs.dtu.dk/services/ProtFun/36 properties and possible gene ontology, ab initio predictions of protein function from sequence. STRING STRING is a database of known and predicted http://string.embl.de/37 protein interactions. The interactions include direct (physical) and indirect (functional) associations Unified Human Interactome (UniHI) Interaction network prediction http://theoderich.fb3.mdc-berlin.de:8080/unihi/home38 GoMiner Evaluation of gene ontology classification for large http://discover.nci.nih.gov/gominer/39 scale data Ontologizer Evaluation of gene ontology classification for large http://compbio.charite.de/index.php/ontologizer2.html scale data, comparison between datasets Bioinformatic Harvester III Harvester crawls and cross-links the following http://harvester.fzk.de/harvester/40 bioinformatic sites: 4DXp, AceView, BLAST, Biocompare, CDART, CDD, ensEMBL, Entrez, FishMap, Galaxy, UCSC GenomeBrowser, gfp-cDNA, Google-Scholar, gopubmed, Harvester42, H-Inv, HomoloGene, iHOP, IPI, MapView, MGI, MINT, Mitocheck, OMIM, PolyMeta, PSORT II, RGD, SMART, SOSUI SOURCE, STRING, TAIR, Unigene, UniprotKB, Wikipedia, WikiProtein Reactome (including SkyPainter) Curated knowledgebase of biologic pathways http://www.reactome.org41 KEGG PATHWAY database Collection of manually drawn pathway maps http://www.genome.jp/kegg/pathway.html42 representing our knowledge on the molecular interaction and reaction networks (also accessible via STRING) iHOP Hyperlinked information about proteins http://www.ihop-net.org/UniPub/iHOP/43 From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

BLOOD, 2 JULY 2009 ⅐ VOLUME 114, NUMBER 1 PLATELET MEMBRANE PROTEOME e15

6 of them (AVPR1A, GPR92, P2RY1, P2RY12, PAR1, and TRPC6 is supposed to be involved in receptor-activated, diacyl- PTGIR) were determined by both studies. Clearly, proteomics is glycerol–mediated cation entry53 in platelets, whereas little is able to complement mRNA profiling by proving the presence (eg, of known about TRPV2. Interestingly, we could not detect TRPC1 on GPR92) for the first time on the protein level for platelet samples. human platelets by proteomic means, although its presence was Apart from signal transduction, membrane proteins also have shown previously for mice.54 A further, although yet uncharacter- additional functions, such as cellular adhesion. By GO terms, ized potential member of calcium channel proteins was identified 86 proteins implicated in cell adhesion have been found and also with TPCN1 (2 pore calcium channel protein 1). TPCN1 has 47 proteins present or involved in cell junctions. Among them are 12 predicted TMDs (by TMHMM 2.0) and 2 cation channel several proteins known to locate to either cell-cell junctions17 or domains as predicted by SMART (aa’s 143 to 319 and aa’s 478 to anchoring junctions16 such as ITGA2B, ITGAV, TLN1, VASP, or 686).55,56 It might function as a voltage-gated Ca2ϩ channel across VCL. Furthermore, ILK (integrin-linked kinase) was identified, the plasma membrane (by similarity; Uniprot annotation). Supple- which interacts with PINCH-1 and PINCH-2 at extracellular matrix menting the information regarding calcium channels, we identified adhesion sites.45 Cellular adhesion has a profound role in the 101 proteins that are reported to bind calcium by GO. These include stability of platelet plugs/aggregates and the current list of proteins integrins as well as, for example, the ER calcium-sensor STIM1.50,51 potentially includes those important for cellular adhesion, although Relative quantification of platelet proteins not currently annotated by GO. As examples, junctional adhesion molecules A and C (JAM1, JAM3), endothelial cell-selective Inherently, mass spectrometry is a qualitative technique used for adhesion molecule, intercellular adhesion molecule 2, and platelet identification of compounds by their fragmentation patterns. How- endothelial cell adhesion molecule can be seen. ever, under certain conditions, quantitative information may be Having no obvious adhesion properties, tetraspanins feature deduced from mass spectrometric data sets. During recent years, a another interesting subgroup of proteins that are supposed to have number of techniques for relative and absolute quantitation of membrane ordering function in various cell types.46,47 A recent proteins by mass spectrometry have been introduced. Although study by Protty et al48 identified 19 tetraspanins in megakaryocytes some approaches rely on chemical labeling, other label-free by mRNA profiling. Upon raising of antibodies, it was possible to techniques have gained interest over recent years. One of them, detect tetraspanin 9 (TSN9) as novel platelet tetraspanin being called exponentially modified Protein Abundance Index (emPAI), colocalized with GpVI in membrane microdomains. In contrast, the relies on spectral counting.24 Mass spectrometric detection of current study features 13 tetraspanin identifications (CD9/37/63/81/ peptides is partially dependent on the concentration of the respec- 82/151, TSN2/4/9/14/15/18/33) of which TSN2 and TSN15 were tive compound. Assuming, a high abundant peptide is detected exclusively identified, although the remaining 11 proteins were more often during a mass spectrometric analysis, this fact enables covered as well by Protty et al.48 However, in addition it was an approximate relative quantification of identified proteins based possible to prove existence of CD37, CD81, and Tspan4 on the on the number of peptide hits identified for each protein. We used a protein level by mass spectrometric sequencing without the need modified version of the emPAI index to generate an approximate for initially raising antibodies. estimation of platelet protein abundance within the current study Platelet function is dependent on defined distributions of ions set in relation to published data. For 935 discrete protein hits within the cell. Most notably are Ca2ϩ ions, which are quickly derived from the shotgun data, the ratio of observable to observed released into the cytoplasm upon activation leading, for example, peptide hits was calculated and results are depicted in Figure 4 (a to cytoskeletal rearrangements. We identified 91 proteins with complete list of emPAI ratios is presented in the supplemental transmembrane ion transporter activity, of which 23 account for ion table). Indeed, the highest emPAI indices were calculated for channels. furthermore, 24 are ion transporters with ATPase activity integrin IIb/IIIa, which is present at approximately 80 000 copies/ such as plasma membrane calcium transporting ATPase1 (PMCA1), platelet.57 In addition, the components of the gpIb-IX-V complex which removes Ca2ϩ ions from the cytosol. Among the 23 ion (ϳ 25 000 copies/platelet gpIb/gpIX and ϳ 12 500 copies/platelet channels, some CLCN isoforms (chloride channel protein, CLCN3, gpV58,59) are present among entries with high emPAI indices CLCN4, CLCN6, CLCN7) as well as CLIC1 (chloride intracellular (Figure 4 top panel) as well. At slightly lower emPAI values, channel protein 1), ABCC4 (multidrug resistance-associated pro- ICAM-2 (3000 copies/platelet60) and integrin alpha-2 (2000-4000 tein 4), and TTYH3 (protein tweety homolog 3) are potentially copies/platelet61) fit into the scheme as well. In terms of low responsible for chloride transport, with TTYH3 being a potentially abundant proteins, the data correlate as well with the copy numbers Ca2ϩ-activated large conductance calcium channel with proposed of gpVI, P2Y12, and P2Y1 with 1000, approximately 600, and implications in cellular signaling.49 Besides 3 known potassium approximately 150 copies/platelet,57 respectively. Therefore, the channels (KCNA2, KCNA3, and KCNK6), a total of 8 Ca2ϩ ion current data set can be potentially used to differentiate between channel proteins were retrieved by GO annotations. Inositol high, medium, and low abundant protein species. Although the 1,4,5-trisphosphate receptor types 1 and 2 are responsible for Ca2ϩ absolute copy number of an individual protein might not always fits release from the ER upon stimulation by inositol 1,4,5-trisphos- with its position in emPAI ranking (eg, CD36 with 20 000 copies/ phate. Meanwhile, platelet ORAI-1 (calcium release-activated platelet61,62; prior gpIBB and gpIBA with ϳ 25 000 copies each59), a calcium channel protein 1) is a store-operated calcium channel general accordance was found. However, a major problem remains: the mediating Ca2ϩ influx to the cytosol following depletion of different reported copy numbers for proteins in the literature. intracellular Ca2ϩ stores and channel activation by the Ca2ϩ sensor, Furthermore, spectral counting may enable quality control of STIM1 (stromal interaction molecule 1).50,51 Besides the well- membrane preparations. As can be seen in the inset of Figure 4 known ATP-activated P2X purinoceptor 1 (P2RX1), 3 transient (bottom panel), we combined the quantitative information of receptor potential (TRP) ion channels were detected (TRPC6, emPAI values with GO annotation for this purpose. Within the top TRPM4, and TRPV2). TRPM4 is a potential calcium-activated 100 abundant proteins of the 2-phase membrane separation, 76 proteins nonselective cation channel and appears to provide a mechanism were intrinsic to a membrane and 52 proteins could be confirmed to allowing for cell depolarization in a Ca2ϩ-dependent manner.52 be plasma membrane constituents. The percentage of proteins From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

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abundance of serum protein in the membrane preparations—in contrast to approximately 60% serum albumin content in the circulating plasma.

Evaluation of protein interactions

Knowledge of the platelet proteome has been constantly increasing over the years. In parallel to the progress of proteome works, bioinformatics tools have been developed by other groups for the large-scale analysis of proteomic data sets. In this context, we used the STRING algorithm67,68 (http://string.embl.de) to assess the scope of already known protein-protein interactions for the com- plete list of 1282 identified proteins. For data evaluation, the complete protein list was submitted as batch data, all evidence levels were accepted, and only hits with high confidence (0.9) were used with a network depth of 1 and an edge scaling factor of 80%. The results are presented in Figure 5 (a scalable image is given in supplemental table). From the network analysis, a clustering of components was clearly observable. The central groups of the data set were composed of adhesion-mediating receptors and kinases or effectors. This result confirms the presence of a large number of known plasma membrane components (several integrins, gpV, gpIX, PECAM-1, etc). Since these receptors initiate a range of downstream cascades, the occurrence of downstream effectors was to be expected as well. Therefore, protein kinases (PKA, PKC, Jak1-3, Src, Syk, etc) were identified alongside Ras-, Rac-, and Figure 4. Relative quantification of proteins by exponentially modified protein Rho-related proteins, which can be easily deduced from the abundance indices (emPAI). Relative quantification for enriched plasma membrane supplied lists in the supplemental table. Since membrane proteins samples was done by spectral counting within the shotgun-based approaches for 935 proteins (supplemental table). emPAI values were compared with literature estima- are connected to the cytoskeleton, for example, via actin-binding tions of protein abundance and showed overall good correlation. The inset shows proteins, a range of actin- and cytoskeleton-associated proteins was gene ontology–based subcellular classifications for groups from high to low emPAI- found as a clustered group as well. They include components of the ranking proteins. Reaching low emPAI values, low abundant plasma membrane components are mixed with contaminants by other compartments. Arp2/3 complex as well as vinculin or VASP, which is a known interactor of actin.69 Furthermore, coatomer and vesicle-associated proteins such as SNAPs and SNAREs were identified as interacting derived from ER, Golgi apparatus, and mitochondria was minor in units within the data set. As can be deduced from Figure 5, also comparison (18, 10, 2, respectively). In addition, GO allows for proteins relating to metabolism, glycosylation processes, as well as multiple localizations, so a protein might be present in Golgi proteins of mitochondrial origin could be found to be tightly apparatus and plasma membrane as well. Tracing the shares of ER, associated by network analysis. Golgi, and mitochondrial contaminations from top 50 to all In general, a first survey of the membrane proteome by 935 discrete protein hits, the share of contaminations constantly STRING already revealed a total of 858 protein-protein interac- rises in comparison with the plasma membrane share. This argues tions present in databases. However, evident from Figure 5, a large for the majority of impurities to be rather low abundant. In turn, the number of proteins were not associated with any other membrane relative percentage of proteins intrinsic to membranes remains or soluble protein. By introducing 250 nodes (see supplemental fairly constant throughout the data sets. Furthermore, the purifica- table for image), representing additional interaction partners, the tion of membranes can also be monitored looking for actin number of interactions could be raised to 2669. These 250 nodes abundance, which is present in high concentrations of up to reflect known interaction partners, which, however, were not part 2 000 000 copies/platelet. The calculated emPAI value of 36.9 for of the current data set. actin within the current preparation indicates in turn an abundance of 20 000 to 25 000 copies. This is in good agreement with the fact that actin may be tightly associated with the plasma membrane63 in Discussion addition to its abundant soluble or cytoskeletal localization. Other proteins such as VASP (ϳ 80 000 copies/platelet64; emPAI 0.86) or Platelet function in thrombosis and hemostasis is enabled by an coagulation factor V (emPAI 0.01)/multimerin 1 (emPAI 1.1) might intricate interplay of various proteins, small messengers, and ions indicate low abundance of soluble cytoplasmic (VASP) and on various levels. Current research on platelet function is driven vesicular proteins. Furthermore, the absence of several marker mostly by studies on individual proteins, many of which are also proteins for white blood cells such as CD4, CD8, CD15, CD16, studied in knockout models or using antibodies. The results of the CD19, as well as the low emPAI value (1.17) for the high abundant current proteome study on membrane proteins can supplement CD45 leukocyte marker and very low levels of leukosialin (which these studies on various levels, especially regarding important is present at 150 000 copies on human lymphocytes,65 but low plasma membrane interface proteins. abundant on platelets66; emPAI 0.46) might argue for a low-level The assembled data set of 1282 proteins, of which 788 proteins contamination by white blood cells—which might possibly be due were not covered by several proteomic approaches before (Figure to platelet-leukocyte aggregates. Moreover, serum albumin was 2A), offers a premier repository for rational design of upcoming detected with a minor emPAI value (1.05) arguing for low studies. Comparison of currently studied components with the From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

BLOOD, 2 JULY 2009 ⅐ VOLUME 114, NUMBER 1 PLATELET MEMBRANE PROTEOME e17

Figure 5. Protein interaction network of 1282 identified proteins. Interaction analysis was performed by STRING (scalable picture can be found in the supplemental table). Several clusters of direct interaction partners are marked and correspond, for example, to adhesion receptors, effector proteins, or vesicular proteins (sorting based on function-derived, eg, by GO terms and Swiss-Prot entry information). However, the majority of proteins are without direct interaction partners, thereby arguing for a more extensive near-membrane proteome.

presented accessions can return formerly unknown members of during membrane purification, or even varying abundance due to functional protein classes in platelets, as shown briefly in the biologic differences in platelet populations. “Functional characterization” section for the tetraspanins. To Furthermore, the presented repository on identified peptides is a deduce suitable targets for functional research and extend the valid source for sequences to be used in antibody generation. scientific value of the presented platelet protein collection, acces- Obviously, proteins may be modified by a multitude of posttransla- sion lists from the supplemental material can conveniently be tional modifications (eg, glycosylation). However, the given pep- evaluated by a multitude of bioinformatic algorithms, of which tides in the current study have been unambiguously identified in some are proposed in Table 2. Examples thereof have already been their indicated form (see supplemental table) and can therefore given by TMD prediction and STRING interaction networks. avoid choice of unsuitably modified sequences for antibody Moreover, STRING data may serve as origin for potential down- generation. Lastly, COFRADIC results were shown to contain stream analysis of signaling pathways by rational search for known valuable information on N-terminal protein sequences, supplement- binding partners, which were so far uncovered in platelets but ing existing database information. known, for example, in other cell types. In addition, shotgun- In total, we estimate the current study to exert a profound derived semiquantitative information offers a first estimation of influence on the platelet research field and stimulate research on protein abundance and thereby influences the choice of future new target proteins for thrombosis and hemostasis. research targets. The immunoglobulin receptor G6B might serve as an example. Although the receptor was initially described on platelets by proteomic approaches in 2005,1,29 its function remained elusive until the recent works of Senis et al.4 The current data set Acknowledgments ranks G6B with an emPAI score of 122 among the high abundant receptors on the platelet surface with possibly profound impact on The financial support by the Ministerium fu¨r Innovation, platelet function. However, no direct conclusions from apparent Wissenschaft, Forschung und Technologie des Landes Nordrhein- abundance to functional importance should be drawn without Westfalen and by the Bundesministerium fu¨r Bildung und further validation. The mass spectrometric detection of a proteins/ Forschung is gratefully acknowledged. Parts of the work were peptides within the given workflow is dependent, for example, on furthermore supported by the Sonderforschungsbereich ionization properties of peptides, individual protein behavior Grant 688 (A.S.). From bloodjournal.hematologylibrary.org at GRAND VALLEY STATE UNIV on November 19, 2013. For personal use only.

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Conflict-of-interest disclosure: The authors declare no compet- Authorship ing financial interests. Correspondence: Albert Sickmann, Institute for Analytical Contribution: U.L., S.W., K.L., and R.P.Z. performed experiments Sciences, Bunsen-Kirchhoff-Str 11, 44139 Dortmund, Germany; and collected/analyzed data; U.L., A.S., D.A.W., and U.W. designed e-mail: [email protected]. the research; and U.L., S.W., K.L., and R.P.Z. wrote the paper. References

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