The Protein Interaction Network of Extracellular Vesicles Derived from

Article pubs.acs.org/jpr

The Protein Interaction Network of Extracellular Vesicles Derived from Human Colorectal Cancer Cells Dong-Sic Choi,†,‡ Jae-Seong Yang,†,§ Eun-Jeong Choi,†,‡ Su Chul Jang,‡ Solip Park,§ Oh Youn Kim,‡ Daehee Hwang,§ Kwang Pyo Kim,∥ Yoon-Keun Kim,‡ Sanguk Kim,*,‡ and Yong Song Gho*,‡ ‡

Department of Life Science and Division of Molecular and Life Sciences, Pohang University of Science and Technology, Pohang, Republic of Korea § School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Republic of Korea ∥ Department of Molecular Biotechnology, Konkuk University, Seoul, Republic of Korea S Supporting Information *

ABSTRACT: Various mammalian cells including tumor cells secrete extracellular vesicles (EVs), otherwise known as exosomes and microvesicles. EVs are nanosized bilayered proteolipids and play multiple roles in intercellular communication. Although many vesicular proteins have been identified, their functional interrelationships and the mechanisms of EV biogenesis remain unknown. By interrogating proteomic data using systems approaches, we have created a protein interaction network of human colorectal cancer cell-derived EVs which comprises 1491 interactions between 957 vesicular proteins. We discovered that EVs have well-connected clusters with several hub proteins similar to other subcellular networks. We also experimentally validated that direct protein interactions between cellular proteins may be involved in protein sorting during EV formation. Moreover, physically and functionally interconnected protein complexes form functional modules involved in EV biogenesis and functions. Specifically, we discovered that SRC signaling plays a major role in EV biogenesis, and confirmed that inhibition of SRC kinase decreased the intracellular biogenesis and cell surface release of EVs. Our study provides global insights into the cargo-sorting, biogenesis, and pathophysiological roles of these complex extracellular organelles. KEYWORDS: exosomes, microvesicles, ectosomes, network biology, proteome, SRC signaling

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from microvesicles.6 However, physical and composition properties of exosomes and microvesicles are similar with moderate difference.3 These uncertainties make it difficult to discriminate exosomes from microvesicles after they are secreted from mammalian cells. Therefore, we refer these vesicles as extracellular vesicles (EVs).7 Evidence has been provided that various types of mammalian cells such as platelets, leukocytes, epithelial cells, endothelial cells, and tumor cells release EVs either constitutively or in a regulated manner.8 EVs have also been found in various body fluids such as plasma, malignant pleural effusion, and urine.9−12 Although their biological roles are not completely understood, EVs have been suggested as extracellular organelles that play pleiotropic functions in intercellular communication.13−18 Through the activation of a receptor and the transfer of membrane proteins, signaling molecules, and mRNAs, EVs stimulate recipient cells for signal transduction, immune modulation, and transformation. This EV-mediated communication is an evolutionarily conserved and a universal process that exists from prokaryotes to eukaryotes.18−21 Furthermore,

INTRODUCTION Mammalian cells secrete membrane vesicles into the extracellular milieu from the plasma and endosomal membrane compartments.1,2 These vesicles, spherical bilayered proteolipids with an average diameter of 40−250 nm, are enriched with various bioactive materials including proteins, lipids, and genetic materials.1−3 Currently, two independent mechanisms for vesicle formation have been proposed:1−3 (1) exosomes (40−100 nm in diameter) are secreted from the endosomal membrane compartment after the fusion of multivesicular bodies with the plasma membrane, or (2) cells shed microvesicles (>100 nm in diameter) directly from the plasma membrane. Cells secrete exosomes and microvesicles at the same time, but their ratios differ depending on the cell type and cell status.1−3 Although their physiological functions are not fully understood, it has been suggested that exosomes and microvesicles do not display the same functionality.4 Exosomes have a density of 1.13−1.19 g/mL and are enriched with tetraspanins (CD9, CD63, and CD81), Tsg101, and Alix.1−3 On the other hand, microvesicles are not enriched with tetraspanins, especially CD63, but harbor the cell surface proteins such as CD59, CD47, and CD55.5 Due to their difference in physical properties, differential and density gradient centrifugations are promising approach for isolation of exosomes © 2011 American Chemical Society

Received: August 30, 2011 Published: December 13, 2011 1144

dx.doi.org/10.1021/pr200842h | J. ProteomeRes. 2012, 11, 1144−1151

Journal of Proteome Research

Article

most intense peaks. The peak repeat count for dynamic exclusion was 1, and the repeat duration was 30 s. The dynamic exclusion duration was set to 180 s, the exclusion mass width was ±1.5 Da, and the list size of dynamic exclusion was 50.

recent studies have drawn attention to the importance of mammalian EVs in clinical applications as diagnostic tools for and vaccines against cancers.22,23 Several hundred proteins of EVs have been identified from various cell types and body fluids by mass spectrometry-based proteomics, Western blotting, and fluorescence-activated cell sorting.10,12,17,24,25 Vesicular proteins differ from whole cellular proteome and are mainly derived from plasma membrane and cytoplasm. EVs are equipped with not only general vesicular proteins, including Rab small GTPases, Alix, TSG101, HSP70, clathrin, and cytoskeleton proteins, but also with cell-surface antigens, immune-modulating cytokines, tetraspanins, proteases, and angiogenic molecules that are required for immune modulation, proliferation and migration of cells, and neovascularization.17 Although proteomic analyses have allowed vesicular proteins to be cataloged, the underlying mechanisms of protein sorting into EVs and the interrelationships between vesicular proteins remain to be discovered. To address this issue, we constructed and analyzed a protein−protein interaction (PPI) network for EVs derived from human colorectal cancer cells, which we will hereafter refer to as the extracellular vesicle PPI network (EPIN). In this study, we describe for the first time how these complex extracellular organelles are organized by PPIs, revealing that EV proteins are closely interconnected via physical interactions and cluster into functional modules involved in the biogenesis and pathophysiological functions of EVs.

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Protein Identification

We combined previous proteomic data sets17 with two replicate data sets from HT29-derived EVs. MS/MS data were analyzed using the computational proteomics analysis system with the X!! Tandem (version Dec-01-2008) search engine.28 The SEQUEST RAW files were converted to mzXML files using the TransProteomic Pipeline.29 MS/MS spectra for ions with charges of +1, +2, and +3 in the converted mzXML files were searched against the SwissProt human database (release 57.12), which contains 20 318 protein entries. The tolerance was set to 3 Da for precursor ions and 2 Da for fragment ions. The number of missed trypsin cleavage sites was set to 2. The oxidation of methionine (15.995 Da), deamination of N-terminal glutamine (−17.027 Da), and dehydration of N-terminal glutamic acid (−18.011 Da) were selected as a variable modification. The Trans-Proteomic Pipeline (version 4.3) was used to provide statistical analysis of protein identification via PeptideProphet and ProteinProphet. In this study, we used a PeptideProphet probability ≥ 0.9 (false discovery rate; 0.014) and a ProteinProphet probability ≥ 0.9 (false discovery rate; 0.010). Proteomic data set is deposited in the PRIDE database (http:// www.ebi.ac.uk/pride) with the accession numbers of 16880. Microarray Analysis

To identify genes expressed in HT29 cells, we obtained a microarray data set from the Gene Expression Omnibus (GSM356976). GCRMA normalization was performed using the R packages affy and gcrma.30 Then, we modeled the distribution of gene expression in the microarray data set into a two-component Gaussian mixture using the following equation:

EXPERIMENTAL PROCEDURES

Mass Spectrometry

EVs were purified from the conditioned medium as described previously.17 Briefly, confluent HT29 cells were washed twice with PBS and then grown in serum-free RPMI-1640 medium (Invitrogen Corporation, Carlsbad, CA) for 24 h. The conditioned medium was centrifuged once at 500g for 10 min and then twice at 800g for 15 min. We then isolated EVs by a combination of ultrafiltration using a 100 kDa hollow fiber membrane to concentrate EVs, ultracentrifugation onto sucrose cushions, and sucrose density gradients to remove nonmembranous proteins, protein aggregates, and denatured EVs. From sucrose density gradients, we finally collected the CD63and CD81-positive fraction (a density of ∼1.16 g/mL). CD63 and CD81 are well-known EV marker proteins.26,27 The purified EVs were then electrophoresed on a 4−20% gradient Novex Tris-glycine gel (Invitrogen Corporation). Each gel was then stained with GelCode Blue Stain Reagent (Pierce, Rockford, IL) and cut into 10 slices of equal size. Tryptic digestion was then performed.17 The digested tryptic peptides were loaded onto a homemade microcapillary C18 column (75 μm × 10 cm). Buffer A consisted of 0.1% formic acid in H2O, and buffer B 0.1% formic acid in ACN. Separation by LC was conducted using linear gradients of buffer B (3−30% over 50 min; 30−50% over 15 min; 50−90% over 5 min) at a flow rate of 250 nL/min. The separated peptides were then analyzed with an LTQ mass spectrometer (Thermo Finnigan, San Jose, CA) equipped with a nano-ESI. The electrospray voltage was set to 2.0 kV, and the threshold for switching from MS to MS/MS was 500 counts. The normalized collision energy for MS/MS was 35% of the main radio frequency amplitude, and the duration of activation was 30 ms. All spectra were acquired in data-dependent mode. Each full MS scan was followed by nine MS/MS scans of the

P(x) = π 0N (x|m0 , σ0) + π1N (x|m1, σ1) where P(x) represents the probability of gene expression value x, N is a Gaussian probability density function, and π0 and π1 are the mixing proportions of N. The sum off π0 and π1 is 1. The model parameters m0 and m1 are the mean expression values in the absent and present groups, respectively. σ0 and σ1 represent the standard deviations in each group. We determined optimal parameters through expectation maximization.31 Whether each gene was “absent” or “present” was determined based on expression values with a 5% false positive rate in the model. Using these criteria, we identified 10 946 expressed genes in HT29 cells. Construction of the EPIN

To map the identified proteins of EVs into the PPI network, all vesicular proteins and expressed genes in HT29 cells were converted to gene symbols. Protein interaction data were gathered from the Human Protein Reference Database (HPRD) (release 7), which contains 37 107 experimentally confirmed PPIs. Using the interaction data, we constructed a PPI network for the vesicular proteins expressed in HT29 cells. The PPI network was visualized using Cytoscape,32 with self-interactions being removed prior to network analysis. Comparison of the EPIN with a Random Network

We compared the number of interactions in the EPIN and a random network. To generate a random network, the same number of proteins was selected as that contained in the EPIN. We counted the number of interactions (except for 1145



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extensively with cold PBS.33 N−Rh−PE-labeled cells were treated with 10 μM PP1, 10 μM PP2, or 10 μM PP3 for 3 h. They were then washed with PBS, fixed through incubation with 4% paraformaldehyde in PBS containing 4% sucrose, mounted, and analyzed using an FV1000 laser scanning confocal microscope (Olympus, Tokyo, Japan). Scanning electron microscopic analysis was also performed. HT29 cells were treated with 10 μM PP1, 10 μM PP2, or 10 μM PP3 for 3 h, washed with PBS, and fixed through incubation with 2.5% glutaraldehyde in PBS. They were then postfixed through incubation with 1% osmium tetroxide for 1 h and dehydrated using a graded series of ethanol solutions (30−100%). After drying in a HCP-2 critical point dryer (Hitachi, Tokyo, Japan), the cells were mounted on specimen stubs, and Pt-coated using a sputtering device. Images were acquired using a JSM-7401F scanning electron microscope (Jeol, Tokyo, Japan).

self-interactions) within the PPI network. Statistical significance was tested by generating 1000 random networks. PPI Enrichment and Gene Ontology Analysis

We identified 608 proteins that form a major component of the EPIN as vesicular proteins and subjected them to PPI enrichment and Gene Ontology analyses. We first analyzed the PPI enrichment and depletion of vesicular proteins in different subcellular compartments. To obtain information on protein localization, we collected, from HPRD, protein lists for eight major subcellular compartments: cytoplasm, endoplasmic reticulum, extracellular, Golgi apparatus, lysosome, mitochondrion, nucleus, and plasma membrane. To evaluate PPI enrichment and depletion, we constructed a PPI network comprising proteins in each subcellular compartment. We calculated the number of the PPIs between proteins in (1) the same subcellular compartment and (2) different subcellular compartments. We compared the number of PPIs with that of a random network. The random network was constructed by permuting information concerning protein localization while maintaining the topology of the PPI network (distribution of degree and clustering coefficient). By generating 1000 random networks, we obtained the nulldistribution of the PPIs of subcellular compartments. Statistical significance was measured by Student’s t-test. In Gene Ontology enrichment analysis of the EPIN, vesicular proteins were categorized according to their localization and classified by their molecular functions and the biological processes in which they are involved based on PANTHER annotations (http://www.pantherdb.org). We conducted enrichment analysis to identify biological processes and molecular functions enriched in EVs (relative to other subcellular compartments) using a binomial test procedure.

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RESULTS

Construction of a PPI Network for EVs

We first built a EPIN for EVs from HT29 human colorectal cancer cells.17 Using information on physical interactions from the HPRD (release 7), we were able to map 274 of the 547 vesicular proteins into the EPIN. A total of 145 (52.9%) of these 274 proteins formed a major component (Figure S1A, Supporting Information). The EPIN has a significantly higher number of physical interactions than randomized networks as shown in Figure S1B (P < 10−10, Supporting Information), suggesting that such high connectivity of the EPIN did not arise by chance, but stemmed from extensive protein interactions. Our observations indicate that EVs are mainly composed of sets of proteins that form physical interactions, but not randomly selected proteins. However, the number of vesicular proteins typically identified in a single proteomic study is relatively small, which hampers their mapping into biological contexts using network approaches. Thus, we undertook two additional proteomic analyses of HT29-derived EVs. We isolated EVs from the serum-free culture supernatant by a combination of differential centrifugation, ultrafiltration using a 100 kDa hollow fiber membrane, ultracentrifugation onto sucrose cushions, and sucrose density gradients as reported.17 By Western blotting, we observed the enrichment of EVassociated marker proteins including CD9, CD63, CD81, Alix, and Tsg101 in the purified EVs as compared with the parental cells (Figure S2, Supporting Information). To avoid potential contamination from serum-derived vesicles and proteins,7,10,17,34 EVs were isolated from the culture supernatant of HT29 cells incubated in serum-free medium. As reported,17 we observed that apoptosis and necrosis were not induced by incubation of HT29 cells in serum-free medium: the overall viability of HT29 cells cultured in the presence of 10% serum and in serum-free medium was 95.13 ± 0.23% and 94.51 ± 0.79%, respectively (Figure S3, Supporting Information). Furthermore, cytochrome c, a mitochondrial protein found in apoptotic bodies, GM130, a protein found in the cis-Golgi apparatus, and histon H2B were not detected in EVs while they were present in the parental cells, suggesting that purified EVs may not contain apoptotic bodies (Figure S2, Supporting Information). However, we could not completely exclude the possibility of contamination with scarce amounts of apoptotic bodies from the few cells undergoing spontaneous apoptosis in the serum-free culture. We undertook two additional nano-LC−MS/MS analyses of highly purified EVs. By combining them with the first proteomic

Immunoprecipitation

A CTNNB1 (catenin beta) antibody was coupled to Protein G Magnetic Agarose Resin (Elpis-biotech, Daejeon, Republic of Korea) using 1 mM disuccinimidyl suberate. The resin was washed with PBS and 1 M glycine (pH 3) to remove uncoupled antibody. HT29 cells were lysed in lysis buffer (50 mM TrisHCl (pH 7.5), 1% NP-40, 0.25% Na-deoxycholate, 100 mM NaCl, 1 mM EDTA) containing protease inhibitors (Roche, Mannheim, Germany). Cell lysates were incubated overnight with resin-coupled antibody, washed with lysis buffer three times, and eluted in 2× SDS loading dye. Western Blotting

Proteins were separated by SDS-PAGE and then transferred to PVDF membranes. Blocked membranes were then incubated with the appropriate antibodies. Immunoreactive bands were visualized using an ECL substrate. EV Biogenesis

HT29 cells were treated for 24 h with 10 μM cytochalasin D (Sigma, St. Louis, MO), 10 μM brefeldin A (Sigma), 10 μM PP1 (Enzo Life Sciences, Farmingdale, NY), 10 μM PP2 (Enzo Life Sciences), or 10 μM PP3 (Calbiochem, La Jolla, CA). All inhibitors were diluted in serum-free RPMI-1640 medium. Conditioned media were harvested and centrifuged at 500g for 10 min, at 800g for 15 min, twice, and at 150 000g for 2 h. Pellets were resuspended in PBS and analyzed by Western blotting. Microscopic Analyses

HT29 cells were incubated with 5 μM N−Rh−PE (Avanti Polar Lipids, Alabaster, AL) for 30 min at 4 °C and washed 1146



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data set,17 we obtained a total of 1364 vesicular proteins (Supplementary Table S1, Supporting Information). We then filtered out the proteins that were not expressed in HT29 cells, which left 1261 vesicular proteins that were confirmed to be expressed in HT29 cells (Figure S4 and Supplementary Table S2, Supporting Information). The EPIN of the 1261 candidate vesicular proteins comprises 1491 interactions between 957 proteins: 304 proteins do not have any PPI in the HPRD. A total of 608 (63.5%) of the 957 proteins in the EPIN form a major component comprising 98.0% of the PPIs (Figure 1A and Supplementary Table S3,

defense, and cell adhesion; mRNA transcription regulation and transcription factor were depleted. Moreover, some enriched functional modules in the EPIN were derived from the plasma membrane and cytoplasm, while others were derived from other subcellular compartments. For example, the plasma membrane-associated enriched biological processes in the EPIN were exocytosis, immunity and defense, and cell adhesion. Collectively, these observations suggest that EVs are distinct from intracellular organelles and compartments. Cosorting of Physically Interacting Cellular Proteins into EVs

Although several cargo-sorting mechanisms have been proposed,37−40 the molecular mechanisms by which proteins are loaded into EVs during their biogenesis are not fully understood. Based on the high connectivity in the EPIN and the significant enrichment of PPIs among vesicular proteins (Figure 1), we speculated that direct PPIs between cellular proteins might be involved in protein sorting during EV formation. We first experimentally validated our hypothesis through coimmunoprecipitation assay of HT29 cell lysates (Figure 3A). An antibody against CTNNB1 (catenin beta) pulldowned CTNNB1 itself, as well as a first interaction partner (EZR), second interaction partners (ACTB, JUP, and MSN), and a fourth interaction partner (LGALS4) in the EPIN. We additionally observed high degrees of PPIs among tetraspanins (CD9, CD63, CD82, and CD151), integrins (ITGA2, ITGAV, ITGA5, ITGB1, ITGB4, and ITGB6), and other vesicular proteins (Figure 3B). Tetraspanin proteins and integrins have been identified as EV marker proteins.1 In the cell, tetraspanins interact with other proteins to form a cluster known as the tetraspanin web, which plays important roles in cell−cell interactions and cell fusion. We found that HT29-derived EVs harbored 26 of the 39 previously identified tetraspanin web proteins (Supplementary Table S4, Supporting Information).41 The tetraspanin web in the EPIN formed several functional modules involved in angiogenesis, cell adhesion, signaling, and vesicle trafficking, as well as clusters comprising G-proteins and proteases (Figure S7, Supporting Information). Our observations support the idea that protein complexes in the cells can be cosorted into EVs.37,38 Furthermore, cytoplasmic proteins may be cosorted with vesicular cargo proteins because of PPIs, and not merely as a result of nonspecific engulfment during EV formation.39

Figure 1. Overview of the extracellular vesicle protein−protein interaction network (EPIN). (A) The EPIN has 957 nodes with 1491 edges, while the HT29 cell PPI network has 5204 nodes with 20 237 edges. Vesicular and other cellular proteins are identified by red and gray circles, respectively. (B) Comparison of the EPIN with a random network. The arrow indicates the observed number of the PPIs in the EPIN.

Supporting Information). The EPIN has a significantly greater number of physical interactions than randomized networks (Figure 1B; P < 10−10). The overall EPIN topology (Figure 1A and Supplementary Table S3, Supporting Information) follows the power-law distribution: EVs have (1) several hub proteins, with many other vesicular proteins having few interacting partners, and (2) well-connected clusters similar to those identified in other cellular networks.35,36

Functional Modules Clustered with Biological Processes in the EPIN

Based on our functional enrichment analyses and literature mining, we mapped enriched biological processes onto the EPIN and defined subnetworks. Proteins with similar functions were connected to each other, forming functional modules in the EPIN (Figure 4 and Figure S8, Supporting Information). For example, we identified a number of proteins frequently studied in the context of EV biogenesis that form functional modules including proteins of the actin cytoskeleton, ADP ribosylation factors (ARFs), endosomal sorting complex required for transport, Rabs, and exocytosis-associated proteins. Regulation of the actin cytoskeleton is essential to EV biogenesis.42 We showed that disruption of the actin cytoskeleton by cytochalasin D significantly increased the release of EVs by HT29 cells (Figure S9A, Supporting Information). The small GTP-binding protein ARF1 activates phospholipase D which generates fusogenic lipids.43 Both ARF1 and phospholipase D activities have been known to be required for EV biogenesis.43,44 We showed that brefeldin A, which specifically inhibits the ARF1,

Comparisons of EVs with Other Subcellular Compartments

We discovered, as in other subcellular compartments, that there was a significant enrichment of PPIs among vesicular proteins (Figure 2A). Since the majority of vesicular proteins (∼70%) are derived from the cytoplasm, mitochondrion, and plasma membrane (Figure S5, Supporting Information), they interacted with those in the cytoplasm or mitochondrion, or on the plasma membrane. We further analyzed the functional annotations of vesicular proteins and found that specific biological processes and molecular functions were highly enriched or depleted (Figure 2B and Figure S6, Supporting Information). Functions that were highly enriched in the EPIN included intracellular protein traffic, cell structure and motility, exocytosis, immunity and 1147



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Figure 2. Comparisons of EVs with other subcellular compartments. (A) Correlations of PPIs between EV proteins and proteins found in other subcellular compartments. Colors indicate the magnitude of enrichment or depletion of PPIs between pairs of subcellular compartments. (B) Colors indicate the statistical significance of enriched or depleted PANTHER annotations of EVs and other intracellular compartments or organelles. ER, endoplasmic reticulum.

Figure 3. Cosorting of physically interacting cellular proteins into EVs. (A) Immunoprecipitation of CTNNB1 (catenin beta) from HT29 cell lysates. A first interaction partner (EZR), second interaction partners (ACTB, JUP, and MSN), and a fourth interaction partner (LGALS4) of CTNNB1 in the EPIN were detected. (B) Tetraspanin web in the EPIN. Blue squares and red circles represent tetraspanin web proteins41 and their first neighbors in the EPIN, respectively. Functional modules of the tetraspanin web in the EPIN are shown in Figure S7 (Supporting Information).

also affected EV biogenesis, as reported previously (Figure S9A, Supporting Information).44

overexpressed and activated in a large number of malignant cancers.45 We observed that EVs harbored unphosphorylated and phosphorylated SRC (Y419) (Figure 5A). More importantly, phosphorylated SRC was enriched in EVs, suggesting that the activation of SRC kinase may be involved in EV biogenesis. We further found that the release of CD81-EVs by HT29 cells was reduced by the inhibition of SRC kinase with PP1 or PP2, but not by the treatment with the control substance PP3 (Figure 5B). Furthermore, these same kinase inhibitors significantly reduced the levels of total and phosphorylated SRC in EVs. In HT29 cells, treatment with PP1

SRC Kinase Signaling in EV Biogenesis

Recent progress in EV biology suggests that various proteins and lipids are involved in the biogenesis of EVs.1 However, the mechanisms by which cells secrete EVs remain unclear. Through a systems biology approach, we identified SRC to be a major functional module (Figure 4 and Figure S8, Supporting Information). SRC signaling has never before been linked with EV biogenesis. SRC is a nonreceptor tyrosine kinase that is 1148



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Figure 4. Functional modules clustered with biological processes in the EPIN. The EV subnetwork maps of functional modules were clustered with biological processes. Proteins with similar functions were connected to each other to form functional modules in the EPIN. A full map of vesicular subnetworks is shown in Supplementary Figure S8 (Supporting Information).

functional interrelationships and the mechanisms of EV biogenesis are unknown.1,3 In this study, we have described how mammalian EVs are organized by PPIs, revealing that vesicular proteins are interconnected via physical interactions and cluster into functional modules. Based on the vesicular network, we were able to identify functional modules that appear to be involved in EV biogenesis (e.g., SRC kinase), as well as other functions including cell adhesion, invasion, angiogenesis, and immunity against tumor growth and metastasis. Moreover, we experimentally validated (1) direct protein interactions between cellular proteins that may be involved in protein sorting during EV formation and (2) the actin cytoskeleton, ADP ribosylation factor, and SRC kinase signaling, the key functional modules identified in this study, play essential roles in EV biogenesis. Although previous proteomic studies have together identified several hundred vesicular proteins,12,17,24,25 there is a concern as to whether these proteins are genuine components of EVs. We initially identified 1364 proteins in five proteomic analyses of EVs derived from HT29 human colorectal cancer cells. By interrogating proteomic data using genomics approach, 1261 vesicular proteins were confirmed to be expressed in HT29 cells. Finally, we were able to map 957 of the 1261 vesicular proteins into the EPIN: 304 proteins do not have any PPI in the HPRD. To the best of our knowledge, this is one of the largest number of vesicular proteins ever identified.46,47 Our approach will form a platform for identifying vesicular proteins in various types of cells. Identification of vesicular proteins with high confidence will be essential for understanding the pathophysiological roles of EVs, as well as their biogenesis and cargo-sorting mechanisms. Considering the size of exosomes or microvesicles, the presence of so many vesicular proteins in the present study suggests the possible presence of different kinds of EVs (i.e., exosomes and microvesicles) and scarce amounts of apoptotic bodies. However, there is no experimental evidence on how many proteins an exosome or a microvesicle can harbor. Moreover, we can speculate that a single cell should secrete many different kinds of exosomes or microvesicles. Therefore, further studies to elucidate how many vesicular proteins are present in a single type of exosomes or microvesicles will be of great value.

Figure 5. SRC kinase signaling in EV biogenesis. (A) HT29-derived EVs harbored unphosphorylated SRC and pSRC (Y419). pSRC (Y419) denotes SRC phosphorylated at Tyr419. (B) Western blotting of EVs prepared from HT29 cells treated with a SRC kinase inhibitor (PP1 or PP2) or a control substance (PP3). CD81 is a marker protein of EVs.27 (C) Confocal microscope and (D) scanning electron microscope images of HT29 cells treated with PP1, PP2, or PP3. In (C), cells were labeled with N−Rh−PE (red), a fluorescent lipid known to accumulate in multivesicular bodies.33 DIC, differential interference contrast. Scale bars: 5 μm in (C), 1 μm in (D).

and PP2, but not PP3, blocked the phosphorylation of SRC (Figure S9B, Supporting Information). None of these chemicals affected the levels of SRC, CD81, or actin. In addition, PP1 and PP2 inhibited the formation of multivesicular bodies (Figure 5C and Figure S10A, Supporting Information) and release of EVs from the cell surface (Figure 5D and Figure S10B, Supporting Information). Together, these observations suggest that SRC signaling plays an important role in the regulation of EV biogenesis.

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DISCUSSION

The shedding of EVs is an evolutionary conserved process that occurs in from simple organism to complex multicellular organisms.18−21 EV-mediated communication is a process that is evolutionarily conserved from archaea to prokaryotes and eukaryotes.18−20 Since its discovery decades ago, EV biology has played a key role in conceptual advances in the field of intercellular communication under physiological and pathological conditions. Although EVs have been studied extensively for several years and proteomic analyses of mammalian EVs have allowed several hundred vesicular proteins to be cataloged, their 1149



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EVs have often been referred to as “cellular dust”. However, growing evidence suggests that they are “extracellular organelles” playing diverse roles in intercellular communication.17 Organelles are, by definition, subcellular structures surrounded by lipid membranes that have functional modules, formed as a result of substantial PPIs between their constituent proteins.48 We have shown that the overall topology of the EV network follows the power-law distribution: EVs have several hub proteins, and well-connected clusters similar to those identified in the networks of other intracellular organelles including phagosomes, autophagosomes, and mitochondria.49−51 Furthermore, we discovered that there is a significant enrichment of PPI among vesicular proteins, as is seen in other intracellular organelles or compartments.48 Moreover, groups of proteins with roles in specific biological processes and molecular functions are highly enriched or depleted in EVs. Some enriched functional modules in the EVs are derived from the plasma membrane and cytoplasm while others are derived from other subcellular compartments. These observations suggest that EVs are extracellular organelles that are distinct from intracellular organelles and compartments. Until now, less attention has been paid to EVs than other cellular organelles. However, recent studies have drawn attention to the importance of both mammalian and bacterial EVs in intercellular communication under pathophysiological conditions, and clinical applications as diagnostic tools for and vaccines against cancers and bacterial infections.18,22,23 By integrating proteomic data and systems biology approaches, we have shown that EVs derived from human colorectal cancer cells harbor clusters of subnetworks whose proteins are extensively interconnected by physical and functional interactions, suggesting that EVs are nanocosmos (i.e., nanosized extracellular organelles) rather than cellular dust. This study provides an integrated view of EVs, not only with regard to their biogenesis and pathophysiological functions but also to their diverse applications as diagnostic tools for and clinical vaccines against cancers.

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ASSOCIATED CONTENT

Supplementary figures and tables. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION

Corresponding Author

*(Y.S.G.) Tel: 82-54-279-2345. Fax: 82-54-279-8609. E-mail: [email protected]. (S.K.) Tel: 82-54-279-2348. Fax: 82-54279-2199. E-mail: [email protected]. Author Contributions †

These authors contributed equally to this work.

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REFERENCES

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ACKNOWLEDGMENTS

We are grateful to the members of our laboratories. This work was supported by Midcareer Researcher Program through NRF grant funded by the MEST (No. 2009-0080709 and No. 20100017496) and the Korea Science and Engineering Foundation (KOSEF) NCRC grant funded by the Korea government (MEST) (No. 2010-0028447). 1150



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The Protein Interaction Network of Extracellular Vesicles Derived from

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