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Article Cite This: ACS Omega 2019, 4, 10649−10661

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Proteomic Analysis of Plasma-Derived Extracellular Vesicles in Smokers and Patients with Chronic Obstructive Pulmonary Disease Isaac K. Sundar,*,† Dongmei Li,‡ and Irfan Rahman† †

Department of Environmental Medicine and ‡Department of Clinical & Translational Research, University of Rochester Medical Center, Rochester 14642, New York, United States

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ABSTRACT: Circulating biomarkers using targeted approaches from human plasma-derived extracellular vesicles (EVs) in the pathobiology of chronic obstructive pulmonary disease (COPD) are an emerging tool. There are several challenges in the field of EV proteomics. We used different EV isolation methods (ExoQuick and Exo-Spin) for purification of human plasma-derived EVs from nonsmokers, smokers, and patients with COPD. Furthermore, characterization of plasma-derived EVs was performed based on their size, particle concentrations, surface markers, and acetylcholinesterase activity. Additionally, we performed labelfree MS analysis of plasma-derived EVs isolated by two different methods for comparison. We performed a thorough analysis of EV markers from the identified peptides and peptide-spectrum matches using the four main databases on EV biomolecules. We found several common EV markers enriched in our data set compared with top 100 plasma-derived EV markers from databases. Using a pairwise comparison, we identified several novel proteins such as CD5L, FN1, CLU, GSN, HABP2, APOD, and EFEMP1 differentially enriched in smokers and patients with COPD compared to nonsmokers. This pilot study demonstrates the importance of EV isolation and characterization from plasma. Thus, it facilitates the identification of novel circulating EV biomarkers as a tool for prognostics, diagnostics, and therapeutics of chronic inflammatory lung diseases.

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INTRODUCTION Extracellular vesicles (EVs) play an important role in normal lung physiology to maintain homeostasis in the airways via intercellular communication.1,2 EVs are classified into three groups primarily based on their size: (1) exosomes (nanosized vesicles 50−150 nm), (2) microparticles (MPs: 100−2000 nm), and (3) apoptotic bodies (1−4 μm). MPs are also known as microvesicles, which originate by budding or shedding from the plasma membrane. Exosomes are characterized by their endosomal origin. EVs are released from different cells such as platelets,3 red blood cells,4 leucocytes,5 epithelial cells,6−10 and endothelial cells11−14 during activation or cellular processes, such as apoptosis as a result of chronic inflammatory responses. Exosomes vary in their surface protein composition [endosomal markers such as tetraspanins (CD9, CD63, and CD81), HSP 70, Alix, TSG101, and MHC classes I and II] and their content depending on their cellular origin.15,16 Generally, EV/exosomal marker proteins from lungs are enriched based on the cell type and their source of origin, such as epithelial cells, 6−9,17 endothelial cells,11−13 and alveolar macrophages.18,19 Prior reports show the presence of endothelial © 2019 American Chemical Society

injury-related circulating endothelial MPs in patients with chronic obstructive pulmonary disease (COPD),11,13 but not much is known about the protein content of the EVs/exosome cargo in smokers and patients with COPD compared to nonsmokers. Evidence from a recent report correlates the rapid decline in lung function FEV1 with high levels of E-selectin as a major circulating endothelial MP in patients with COPD.13 It is possible that EV/exosome particle counts, concentrations, and protein cargos are enriched differentially during exposure to inhaled toxicants, such as tobacco smoke, that may be identified as novel circulating EV biomarkers. COPD is a chronic inflammatory lung disease, with cigarette smoking being the main etiological risk factor. EVs play an important role during several biological and cellular processes, such as immune-inflammatory response, cellular senescence, autophagy, thrombosis, endothelial dysfunction, tissue remodeling, and angiogenesis.20−24 Epithelial cellular injury in Received: April 4, 2019 Accepted: June 5, 2019 Published: June 19, 2019 10649

DOI: 10.1021/acsomega.9b00966 ACS Omega 2019, 4, 10649−10661

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response to inhaled toxicants plays a prime role in the pathogenesis of COPD.25,26 The total numbers of apoptotic epithelial and endothelial cells were significantly increased in the lungs of patients with COPD/emphysema compared with lungs of healthy individuals.27−29 Lung cellular senescence in alveolar epithelial, fibroblasts, and endothelial cells leads to premature lung aging in COPD.30,31 Injured and activated cells in the lung contribute to the major release of chemical mediators into circulation, such as alarmins and microvesicles.32 The chemical mediators released play an important role in the modulation of systemic immune responses.33−35 Thus, EVs play a crucial role in the maintenance of lung homeostasis and physiology by cell-to-cell communication.1,24 In the present study, we used standard methods for isolation and characterization of EVs from stored human plasma (nonsmokers, smokers, and patients with COPD) combined with LC−MS/MS-based label-free quantification of EV/ exosomal proteins. This pilot study offers the ability to understand the challenges we face during EV proteome characterization using mass spectrometry from plasma-derived EVs. Thus, EV biomarkers can be useful for the diagnosis, prognosis, and therapeutics of various smoking-related chronic inflammatory lung diseases [e.g., COPD, asthma, and idiopathic pulmonary fibrosis (IPF)].

Figure 1. Schematic for isolation, characterization, and proteomic profiling of plasma-derived EVs. We used human-stored plasma from nonsmokers, smokers, and patients with COPD for isolation of EVs by using two different methods (ExoQuick and Exo-Spin). Characterization of EVs based on their morphology, particle size, and concentrations using TEM, NanoSightNanoparticle tracking analysis, exosomal/EV surface markers by slot-blot analysis and AChE activity using FluoroCet. Enrichment of EV proteins was determined by SDS-PAGE analysis using stain-free gels. EV samples were run on SDS-PAGE gel for 5 minutes; then, the protein bands were excised from the gel, followed by trypsin digestion and LC−MS/ MS analysis for identification of EV biomarkers.

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RESULTS Isolation and Characterization of Plasma-Derived EVs. Previous studies have shown that EVs such as exosomes and microvesicles that contain proteins and miRNAs are novel modulators of disease pathogenesis and intercellular cross talk between cells.1,22 Environmental cues, such as cigarette smoke, airborne pollutants, lipopolysaccharides, and bacterial and viral infections drive the release of EVs from lungs and the transfer of their contents, systemically implicating their role in the pathogenesis of chronic inflammatory lung diseases. Until now, no study has been conducted to identify the proteome profiles in plasma-derived EVs from smokers and patients with COPD compared to nonsmokers (healthy controls). We hypothesize that cigarette smoke can alter the particle size, concentration, and content of EVs (proteins) in systemic circulation, which may play an important role in pathogenesis of COPD. In this study, we show a comprehensive analysis of EV isolation, characterization, and proteomics profiles of human plasmaderived EVs from nonsmokers, smokers, and patients with COPD (Figure 1). EVs were isolated from stored plasma samples using the ExoQuick or Exo-Spin method as described in the Materials and Methods section. Transmission electron microscopy (TEM) analysis confirmed cup-shaped morphology of EVs isolated from plasma samples of nonsmokers, smokers, and patients with COPD. TEM analysis confirmed the presence of contaminating lipoprotein aggregates in isolated EVs (Figure 2A). Next, we used nanoparticle tracking analysis (NTA: NanoSight 300) to determine the total number of particles, size, or distribution of EVs in isolated samples. We did not observe any significant change in the concentration, size, and distribution of EVs analyzed by NTA between nonsmokers, smokers, and patients with COPD (Figure 2B), including protein concentration measured using micro BCA. Some of the results have been reported in the form of a conference abstract.36 The summary for NTA analysis results and protein concentrations are tabulated (Table 1). We used stain-free gel to evaluate the total protein content of isolated EVs. We found proteins that were relatively

enriched in EVs from nonsmokers, smokers, and patients with COPD that belong to different size ranges [low, medium, and high molecular weights (MWs)] (Figure 3A). We have also provided total protein gel images from EVs isolated using the Exo-Spin method before and after the IgG depletion step, which shows the difference in EV protein enrichment (Figure S1). We used slot-blot analysis to determine EV markers present in isolated EV preparations. We found enrichment of EV surface markers such as Rab-5b, TSG101, and Alix (Figure 3B) that have been shown to play an important role in vesicle trafficking and multivesicular body biogenesis. Additionally, we used acetylcholinesterase (AChE) activity quantification to indirectly estimate the quantity of exosomes in isolated plasmaderived EVs. We observed a trend of increased AChE activity, a specific enzyme associated with the exosome membrane in EVs, from smokers, but was not significant compared to nonsmokers and patients with COPD, suggesting enrichment of EVs in our preparations (Figure 3C). Proteomic Profiles of Plasma-Derived EV Proteins and Database Search. LC−MS/MS proteomic profiling of human plasma-derived EVs from nonsmokers, smokers, and patients with COPD yielded identification of 478 total proteins based on the peptide counts and peptide-spectrum match (PSM)-combined data sets from batches 1 (ExoQuick) and 2 (Exo-Spin). Of the 478 proteins/peptides identified, unique proteins identified by MS analysis that were present in batch 1 and 2 samples were 161 and 90, respectively. We also identified 227 proteins that were present in common between both the data sets from batches 1 and 2 as shown in the Venn diagram (Figure 4A). Then, we performed database search/analysis using the combined data set from batches 1 and 2. Of the total 478 proteins identified, only 425 peptides/PSMs were proteins associated with EVs/exosomes based on database searches [Vesiclepedia, ExoCarta, Plasma Proteome Database (PPD) and EVpedia]. 10650


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Figure 2. Isolation and characterization of plasma-derived EVs. (A) Representative TEM images of plasma-derived EVs isolated using the ExoQuick method (scale bar, 100 or 200 nm). Arrows indicate the presence of plasma-derived EVs identified by TEM analysis from the nonsmoker, smoker, and patient with COPD (B) representative graphs of NTA of plasma-derived EVs from nonsmoker, smoker, and COPD, indicating concentration and size distribution of isolated particles. Size distribution and concentration of particles including protein concentration of isolated EVs are summarized in Table 1 (n = 4−5/group).

Table 1. Particle Concentration, Size (Mode and Mean), and Protein Concentration of Plasma-Derived EVs Isolated by the ExoQuick Method groups

particle concentration × 1011

size range (nm)

mode size (nm)

mean size (nm)

protein concentration (mg/mL)

nonsmokers smokers COPD

7.97 ± 3.72 7.51 ± 5.18 7.19 ± 3.84

72−594 96−554 86−693

101.18 ± 8.19 110.00 ± 13.71 104.85 ± 7.93

134.83 ± 10.65 146.88 ± 10.07 139.78 ± 16.75

2.82 ± 1.23 3.89 ± 1.38 3.10 ± 0.69

common from this study and the PPD (Figure 5A). The identified common exosomal/EV markers from different database searches were then analyzed using the STRING database. Protein−protein interaction (PPI) networks were created using the identified 28 (this study vs Vesiclepedia), 23 (this study vs ExoCarta), and 118 (this study vs PPD) proteins detected in exosome/EV studies curated in databases (Figures 4B−D and 5B). Proteomic Profiles of Plasma-Derived EV Protein Markers for Gene Ontology and Network Analysis. To obtain a functional overview of the EV/exosome marker proteome, we queried the gene ontology (GO) database using the STRING tools for EV markers identified in this study that are common among top 100 or more EV/exosome markers present in Vesiclepedia/ExoCarta/PPD (Tables S1−S3). As expected, when using biological process GO, the EV proteome shows enrichment of wound healing, platelet degranulation, platelet activation, regulation of body fluid levels, blood coagulation, vesicle-mediated transport, protein activation cascade, complement activation, and response to stress common among this study versus other database searches (Tables S1−S3). When using the molecular functions GO, we found that the annotation of the EV proteome is heavily enriched for binding functions [protein, binding, RNA binding, poly(A) RNA binding, heterocyclic compound binding, organic cyclic compound binding, protein complex binding,

Next, we used the UniProt accession or gene symbol of the 425 identified proteins for comparison along with other existing complete lists or top 100 exosomal/EV markers downloaded from databases. Of the top 100 EV markers listed in Vesiclepedia, 28 of the EV markers [ACTB, ANXA2, ENO1, ANXA6, ANXA1, TLN1, GSN, PKM, LGALS3BP, A2M, KRT1, KRT10, PFN1, YWHAE, C3, HSPA5, ALB, PPIA, ACTN1, MYH9, GAPDH, TFRC, HIST1H4A, LDHB, CCT3, FLNA, FN1, and HSP90AA1] identified in this study were present in common (Figure 4B,C and Table 2). Similarly, when we compared the top 100 exosomal markers listed in ExoCarta, 23 of the EV markers [ACTB, ANXA2, ENO1, ANXA6, ANXA1, PKM, LGALS3BP, A2M, PFN1, YWHAE, HSPA5, ALB, PPIA, MYH9, GAPDH, TFRC, HIST1H4A, THBS1, LDHB, TUBA1C, CCT3, FLNA, and HSP90AA1] from this study were present in common (Figure 4B,D and Table 2). When we compared the top 100 EV markers from the EVpedia database, we found 35 of the EV markers [ACTB, ANXA2, ENO1, ANXA6, ANXA1, TLN1, KRT9, GSN, PKM, LGALS3BP, PRDX6, A2M, KRT1, KRT10, PFN1, APOE, YWHAE, C3, HSPA5, ALB, PPIA, ACTN1, CLU, MYH9, GAPDH, TFRC, HIST1H4A, THBS1, LDHB, FLNA, GSTP1, FN1, HSP90AA1, HBA1, and KRT2] that were common among EVpedia and this study (Table 2). Additionally, we also compared the list of EV markers from the PPD and found that out of 318 EV markers listed in the PPD, 118 were present in 10651


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Figure 3. Total protein content of isolated EVs, exosomal/EV markers, and quantification of exosomes based on AChE activity. (A) Representative stain-free gel showing relative enrichment of total proteins based on their MW compared to protein standards shown (lane 1) present in plasmaderived EVs by the ExoQuick method from nonsmokers, smokers, and patients with COPD. (B) Representative images from slot-blot analysis show enrichment of vesicle trafficking marker (Rab-5b) and endosomal sorting markers (TSG101 and Alix) in plasma-derived EVs. (C) Amount of exosomes/EVs isolated from nonsmokers, smokers, and patients with COPD shows increased AChE activity as measured by using FluoroCet (n = 4−5/group).

Figure 4. Venn diagram and STRING PPI network using the proteins identified in this study vs top 100 EV/exosomal markers from the database. (A) Venn diagram shows the total number of proteins identified by two different EV isolation methods and the proteins that were commonly identified using MS analysis (B) Venn diagram shows the overlap of proteins identified in this study (425) from plasma-derived EVs with those reported in Vesiclepedia (28) and ExoCarta (23) database. The top 100 proteins mostly identified in the EVs/exosomes were downloaded from the Vesiclepedia and ExoCarta databases for comparison along with proteins identified in this study and analyzed by the STRING database. (C) STRING PPI network connectivity of the human plasma-derived EV protein cluster compared to Vesiclepedia. The network contains 49 edges (vs 11 expected edges); a clustering coefficient of 0.726; an enrichment p-value < 0.001. (D) STRING PPI network connectivity of the human plasmaderived EV protein cluster compared to ExoCarta. The network contains 41 edges (vs 8 expected edges); a clustering coefficient of 0.779; enrichment p-value < 0.001; confidence score threshold was set at 0.7 (high) for both analyses.

and cytoskeletal protein binding] and inhibitory or regulatory activity (endopeptidase inhibitor activity, peptidase regulator activity, enzyme inhibitor activity, and serine-type-endopeptidase inhibitor activity) (Tables S1−S3). The cellular component GO revealed significant enrichment for extracellular exosome, extracellular region, blood MP, focal adhesion, extracellular space, myelin sheath, and melanosome. KEGG

pathways network revealed enrichment of several distinct pathways such as salmonella infection, phagosome, glycolysis/ gluconeogenesis, regulation of actin cytoskeleton, viral carcinogenesis, focal adhesion, complement and coagulation cascades, Staphylococcus aureus infection, pertussis and prion diseases (Tables S1−S3). Overall, these results collectively support the notion that the plasma-derived EVs from nonsmokers, 10652


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Benjamini−Hochberg procedure to control the FDR at 5%, only a few proteins were significantly enriched based on the identified PSMs among smokers versus COPD comparisons (CD5L, APOM, APOD, LPA, and CFH). Proteomic Profiles of Plasma-Derived EV Proteins (Peptides/PSMs) from Nonsmokers, Smokers, and Patients with COPD for GO and Network Analysis. To examine the differences among the three groups (nonsmokers, smokers, and patients with COPD) based on the enrichment of PSMs/peptides for identified proteins, we used a general linear model to fit the proteomic data after adjusting for the batch effect. Linear contrasts were conducted for pairwise comparisons (nonsmokers vs smokers; nonsmokers vs COPD; and smokers vs COPD). The summary of significant PSMs and peptides from pairwise comparison analyses was used for STRING PPI analysis and GO (Tables S6 and S7). Using an interaction score threshold of 0.4 (medium confidence), the STRING PPI analysis for PSMs from nonsmokers versus smokers comparison yielded a clustered network with a clustering coefficient of 0.387 containing 24 nodes with 43 edges (expected number of edges 1), indicating significant interaction (Figure 7A). Enrichment analysis reveals significant GO biological process terms in the network, such as complement activation, humoral immune response, defense response, response to stress, and acute-phase response; GO molecular function terms for the network include peptidase regulatory activity, endopeptidase inhibitor activity, serine-type endopeptidase inhibitor activity, heparin binding, and enzyme inhibitor activity; GO cellular components such as blood MPs, extracellular space, extracellular region, extracellular exosome, and plasma lipoprotein particles. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway with significance in the network includes complement and coagulation cascades and other bacterial infections and diseases (S. aureus infection, pertussis, systemic lupus erythematosus, and prion diseases) (Table 3). Similarly, using an interaction score threshold of 0.4 (medium confidence), the STRING PPI analysis for PSMs from smokers versus COPD comparison yielded a clustered network with a clustering coefficient of 0.453 containing 30 nodes with 44 edges (expected number of edges 3), indicating a significant interaction which was very similar to that shown in Figure 7B. Enrichment analysis reveals significant GO biological processes, molecular functions, and cellular component terms in the network including KEGG pathways that were very similar to what was shown earlier with a few exceptions (Table 4). Additionally, we conducted the same type of analysis using identified peptides that showed significant differences between nonsmokers versus smokers and smokers versus COPD (Figure S4, Tables S6 and S7). We found very few PSMs or peptides that were significantly enriched in nonsmokers versus COPD comparison and hence did not perform STRING PPI analysis (Tables S4 and S5).

Table 2. Summary of Identified EV Markers from This Study Compared to Top 100 EV/Exosomal Markers from Databases SI. no.

this study vs Vesiclepedia

this study vs ExoCarta

this study vs EVpedia

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

A2M ACTB ACTN1 ALB ANXA1 ANXA2 ANXA6 C3 CCT3 ENO1 FLNA FN1 GAPDH GSN HIST1H4A HSP90AA1 HSPA5 KRT1 KRT10 LDHB LGALS3BP MYH9 PFN1 PKM PPIA TFRC TLN1 YWHAE

A2M ACTB ALB ANXA1 ANXA2 ANXA6 CCT3 ENO1 FLNA GAPDH HIST1H4A HSP90AA1 HSPA5 LDHB LGALS3BP MYH9 PFN1 PKM PPIA TFRC THBS1 TUBA1C YWHAE

A2M ACTB ACTN1 ALB ANXA1 ANXA2 ANXA6 APOE C3 CLU ENOl FLNA FN1 GAPDH GSN GSTP1 HBA1 HIST1H4A HSP90AA1 HSPA5 KRT1 KRT10 KRT2 KRT9 LDHB LGALS3BP MYH9 PFN1 PKM PPIA PRDX6 TFRC THBS1 TLN1 YWHAE

smokers, and patients with COPD were enriched in EVs (nanosized vesicles) based on the presence of different commonly identified EV/Exosomal markers. Proteomic Profiles of Plasma-Derived EV Proteins (Peptides/PSMs) from Nonsmokers, Smokers, and Patients with COPD for Hierarchical Cluster Analysis. We performed unsupervised hierarchical clustering to identify potential EV-associated biomarkers (peptides/PSMs) in smokers and patients with COPD compared to nonsmokers (Figure S2). We found differences in the enrichment of peptides/PSMs from batch 1 samples versus batch 2 samples as shown in the hierarchical cluster analysis. We used the batch-normalized PSMs and peptides that were significant but differentially enriched among nonsmokers versus smokers, nonsmokers versus COPD, and smokers versus COPD comparisons for hierarchical clustering (Figures 6 A−C and S3). Our data showed differences in enrichment of novel proteins including several EV markers, extracellular matrix (ECM) proteins, immunoglobulins, and lipoproteins among smokers and COPD compared to nonsmokers (Tables S4 and S5). When the raw p-values were adjusted using the

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DISCUSSION The lung epithelial cells are among the major cell types that contribute to the source of EVs in normal homeostasis.7,9,17,19,37 It remains unclear whether specific lung cell types, such as the alveolar and bronchial epithelial cells, or fibroblasts along and/or endothelial cells, contribute to the increase in the number of circulating EVs in smokers and patients with COPD during the pathogenesis of chronic lung diseases. Isolation and phenotypic characterization of EVs from 10653


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Figure 5. Venn diagram and STRING PPI network using the proteins identified in this study vs top 100 EV markers from PPD. (A) Venn diagram shows the overlap of proteins identified in this study (425) from plasma-derived EVs with those reported in PPD (118). The top 100 proteins mostly identified in the EVs/exosomes were downloaded from the PPD for comparison along with proteins identified in this study and analyzed by the STRING database. (B) STRING PPI network connectivity of human plasma-derived EV protein cluster compared to PPD. Network contains 608 edges (vs 52 expected edges); a clustering coefficient of 0.626; enrichment p-value < 0.001. The confidence score threshold was set at 0.7 (high) for the analysis.

Figure 6. Hierarchical clustering analyses of plasma-derived EVs based on PSM counts. The intensity heatmap of PSM counts for significant proteins identified by LC−MS/MS proteomics among (A) nonsmokers vs smokers, (B) nonsmokers vs COPD, and (C) smokers vs COPD groups.

biofluids can be a useful biomarker for diagnosis, prognosis, and treatment of smoking-related chronic lung diseases.2,34,35,38 Previous reports show evidence that a stressor, such as cigarette smoke exposure, can alter the composition of EVs, thereby playing an important role in modulating airway remodeling in COPD.23 In this study we, for the first time, show isolation and phenotypic characterization of human plasma-derived EVs from nonsmokers, smokers, and patients with COPD using a proteomics approach. Here, we used TEM as a standard tool for characterizing EV morphology and size from nonsmokers, smokers, and patients with COPD on EVs isolated using the ExoQuick method. We found lipoprotein particles co-isolated with EV preparation as observed in TEM images. We know that lipoproteins such APOA, APOB, and APOE have been reported to be present on exosomes, and plasma-derived EVs can be covered with lowdensity lipoproteins.39,40 Additional methods such as the NTA and indirect quantification of exosomes by AChE activity were used to accurately quantify and visualize plasma-derived EV particles (particle size and concentration) in this study. We did not observe any change in EV size, particle counts, and concentration in all the three groups (nonsmokers vs smokers

vs COPD), but the AChE activity data showed a trend toward increase in smokers compared to COPD and nonsmokers. It remains unclear whether smoking history, aging, and decline in lung function has any correlation with difference in the enrichment of EV particles and their phenotypic characteristics. Our EV preparations were confirmed for enrichment of some specific surface markers, particularly Rab-5b involved in vesicle trafficking, ALG-2 interacting protein X (Alix), and tumor susceptibility gene 101 (TSG101) that play an important role in the biogenesis of multivesicular bodies. Proteomic profiling of plasma-derived EVs from two different isolation methods showed differences in their enrichment of EV markers (identified peptides/PSMs) among nonsmokers versus smokers versus COPD by ExoQuick compared with nonsmokers versus COPD by ExoSpin. We observed significantly greater enrichment of several serum immunoglobulins and lipoproteins from EV proteome isolated by ExoQuick (precipitation method). Isolation of EVs from plasma and serum is important to characterize novel circulating EV biomarkers for diseases. Recent reports suggest that lipoprotein particles are highly abundant in the circulation and are found in most of the EV preparations at several fold 10654


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Figure 7. STRING PPI network using the proteins identified in this study vs top 100 EV markers from PPD. (A) STRING PPI network connectivity of the human plasma-derived EV protein cluster compared between nonsmokers vs smokers based on PSM counts. The network contains 43 edges (vs 1 expected edges); a clustering coefficient of 0.387; enrichment p-value < 0.001. The confidence score threshold was set at 0.4 (medium) for the analysis. (B) STRING PPI network connectivity of the human plasma-derived EV protein cluster compared between smokers vs COPD based on PSM counts. The network contains 44 edges (vs 3 expected edges); a clustering coefficient of 0.453; enrichment p-value < 0.001. The confidence score threshold was set at 0.4 (medium) for the analysis.

Table 3. GO Enrichment Analysis for Proteins Based on PSMs Identified in This Study That Are Significantly Different among Nonsmokers vs Smokers Analyzed by the STRING Database pathway ID GO: GO: GO: GO: GO:

0006956 0006959 0006952 0006950 0006953

GO: 0061134 GO: 0004866 GO: 0004867 GO: 0008201 GO: 0004857 GO: GO: GO: GO: GO: 4610 5150 5133 5322 5020

0072562 0005615 0005576 0070062 0034358

pathway description

count in gene set

Biological Process (GO) complement activation 6 humoral immune response 7 defense response 13 response to stress 17 acute-phase response 5 Molecular Function (GO) peptidase regulator activity 7 endopeptidase inhibitor 6 activity serine-type endopeptidase 4 inhibitor activity heparin binding 4 enzyme inhibitor activity 5 Cellular Component (GO) blood microparticle 13 extracellular space 21 extracellular region 22 extracellular exosome 19 plasma lipoprotein particle 6 KEGG Pathways complement and 5 coagulation cascades S. aureus infection 4 pertussis 3 systemic lupus 3 erythematosus prion diseases 2

Table 4. GO Enrichment Analysis for Proteins Based on PSMs Identified in This Study That Are Significantly Different among Smokers vs COPD Analyzed by the STRING Database

false discovery rate 1.08 4.81 1.01 2.83 2.83

× × × × ×

pathway ID

10−7 10−7 10−6 10−6 10−6

7.97 × 10−6 2.98 × 10−5 0.00299 0.0154 0.0194 3.53 1.45 3.24 5.61 4.92

× × × × ×

10−21 10−20 10−11 10−11 10−10

GO: GO: GO: GO: GO:

0072376 0006959 0006956 0002376 0016064

GO: GO: GO: GO: GO:

0005539 0008201 0070891 0008289 0004252

GO: GO: GO: GO: GO:

0005615 0072562 0005576 0070062 0034358

4.06 × 10−6

4610

5.03 × 10−5 0.00676 0.0127

5150 5133 5322

0.0466

5020

higher.39,41 To overcome this, we used another isolation method using Exo-Spin to isolate EVs combined with the IgG depletion step to minimize detection of serum immunoglobulins and contaminating lipoproteins in our EV preparations by LC−MS/MS analysis.

pathway description

count in gene set

Biological Process (GO) protein activation cascade 7 humoral immune response 8 complement activation 6 immune system process 16 immunoglobulin mediated 6 immune response Molecular Function (GO) glycosaminoglycan binding 6 heparin binding 5 lipoteichoic acid binding 2 lipid binding 7 serine-type endopeptidase 4 activity Cellular Component (GO) extracellular space 26 blood microparticle 13 extracellular region 27 extracellular exosome 23 plasma lipoprotein particle 7 KEGG Pathways complement and 6 coagulation cascades S. aureus infection 5 pertussis 5 systemic lupus 4 erythematosus prion diseases 3

false discovery rate 6.21 6.35 1.58 1.27 1.27

× × × × ×

10−8 10−8 10−7 10−6 10−6

0.0012 0.0041 0.0128 0.0157 0.023

2.30 8.25 1.22 5.59 1.12

× × × × ×

10−25 10−20 10−13 10−13 10−11

1.72 × 10−7 1.54 × 10−6 4.81 × 10−6 0.00075 0.00113

We compared proteomic profiles from batch 1 and batch 2 isolation methods to determine the difference in enrichment of EV markers. Proteomics analysis revealed detection of 388 and 317 unique peptides/PSMs, respectively, from batches 1 and 2. ExoQuick and Exo-Spin isolation methods shared in common 10655


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patients with COPD. Only CD5L showed a significant difference in the enrichment of PSMs among nonsmokers versus smokers and smokers versus COPD comparisons. CD5L and LGALS3BP are described as secretory proteins from the superfamily of scavenger receptor cysteine-rich proteins expressed by cells in lymphoid tissues and may be involved in the regulation of immune response through monocytes.47−49 We did not observe any significant difference in the enrichment of peptides/PSMs for LGALS3BP. LGALS3BP, a major sialoglycoprotein previously identified in exomeres (nonmembranous nanoparticles ∼35 nm)50 and EVs,51 is known to play a crucial role in modulating cellular communications and immune responses52,53 present in plasmaderived EVs. This ligand, if enriched in nonsmokers, smokers, and COPD, could mediate the interaction of EVs with target cells via proteins such as collagens, fibronectin, galectin-3, and integrin β-1.54 Recently, THBS1 was among the four EVassociated proteins (SRGN, TPM3, and HUWE1) that distinguished adenocarcinoma patients from controls. Hence, EV-derived proteins from human plasma act as a source for biomarker assessment, which will complement other approaches used for tumor diagnostics.55 The summary of the top 100 most commonly identified EV markers based on their peptides/PSMs from all the database searches was included (Tables S8 and S9). A pairwise comparison using significant PSMs from nonsmokers versus smokers and smokers versus COPD showed enrichment of similar EV marker proteins identified to be present in EVpedia. We found several of the lipoproteins (APOL1, LPA APOB, CETP, CLU, and SAA4) and complement activation and coagulation cascade proteins (C1QA, C1QC, C1S, CFB, CLU, and SERPINA5) identified based on significant PSMs in nonsmokers versus smokers. Similarly, when we compared significant PSMs in smokers versus COPD that were differentially enriched, the complement activation and coagulation cascade proteins mentioned above were present in common along with a few additional proteins (C1QB, CFH, and F11). It remains unclear if complement factors and proteins involved in the activation cascade are significantly enriched in EVs of smokers and patients with COPD compared to nonsmokers. It is worth noting to know that EV proteins (peptides/PSMs) differentially enriched from nonsmokers versus COPD is very low compared to nonsmokers versus smokers and smokers versus COPD comparison groups. This did not allow us to perform the PPI network using the STRING database. Prior evidence supports elevated plasma and sputum complement factor H levels in COPD associated with milder airway obstruction in patients with stable COPD and increased sputum complement (C3a and C5a) levels during COPD exacerbation linked with recovery time.56,57 Interestingly, we found two novel ECM proteins hyaluronan-binding protein (HABP2) and EGF-containing fibulinlike ECM protein (EFEMP1), significantly enriched in COPD compared to nonsmokers identified based on their PSMs and peptides, respectively. Both hyaluronan (HA) and HA-binding proteins were shown to play key roles in various lung diseases.58 HA-binding proteins (CD44, TLR4, HABP2, and RHAMM) are present in diverse sites including the blood, ECM, plasma membrane of the cell, cytosol, and nucleus.59 HABP2 is also known as factor VII-activating protease (HAbinding extracellular serine protease) and is involved in the extrinsic pathway of blood coagulation.58 The levels of HABP2

227 peptides/PSMs as EV markers. Unsupervised hierarchical cluster analysis showed very distinct enrichment of unique peptides/PSMs between batch 1 versus batch 2 among different experimental groups. Our data suggest that one should be very cautious to combine data from different EV isolation methods even if there is a slight modification in their preparation before or after MS analysis. We performed detailed database searches (Vesiclepedia, ExoCarta, EVpedia, and PPD) using the combined data sets to validate the fact that our EVs/ exosomes are enriched with several of the top 100 EV/ exosomal markers. We found 23−35 different EV/exosomal markers that were identified in common among our entire data set compared to EV databases (Vesiclepedia, ExoCarta, and EVpedia). The most enriched EV markers (top 20 identified peptides/PSMs) present in nonsmokers, smokers, and patients with COPD common among the top 100 known EV/exosomal markers include α-2-macroglobulin (A2M), complement C3 (C3), apolipoprotein E (APOE), fibronectin (FN1), serum albumin (ALB), gelsolin (GSN), thrombospondin-1 (THBS1), clusterin (CLU), 78 kDa glucose-regulated protein (HSPA5), α-actinin-1 (ACTN1), keratin, type I cytoskeletal 9 and 10 (KRT9 and KRT10), keratin, type II cytoskeletal 2 epidermal (KRT2), galectin-3-binding protein (LGALS3BP), talin-1 (TLN1), transferrin receptor protein 1 (TFRC), actin, cytoplasmic 1 (ACTB), hemoglobin subunit α (HBA1), keratin, type I cytoskeletal 13 (KRT13), and profilin-1 (PFN1) that were detected in at least three or more samples. A previous report suggests that low abundance plasma proteins reflect on the severity of lung remodeling.42 We found a few of the plasma proteins that were shown to be differentially expressed in COPD determined by GeLC−MS/ MS42 identified in our data set, such as c-reactive protein, fibrinogen α, β, γ (FGA, FGB, FGG), and coagulation factors V, IX, XI, XII, and XIII A and B (F5, F9, F11, F12, F13A1, and F13B). Plasma fibrinogen is qualified as a prognostic biomarker for use in drug development of COPD.43 There is a prerequisite to identify additional novel biomarkers for potential qualifications to use for the diagnosis and treatment of the chronic inflammatory lung disease. Linear contrastbased pairwise comparison of identified peptides/PSMs revealed fibronectin and clusterin among the top 100 EV markers differentially enriched in nonsmokers versus smokers and smokers versus COPD comparisons. In a recent report, EVs isolated from senescent and nonsenescent fibroblasts were shown to induce an invasive phenotype in recipient fibroblasts due to the presence of fibronectin on the surface of EVs.44 Fibroblast invasion is possibly occurring via fibronectin bound on the EV surface that binds to integrin α5β1 and thereby activates invasion-signaling molecules such as FAK and Src kinases.45 We found altered enrichment of fibronectin in plasma-derived EVs isolated from smokers and COPD compared to nonsmokers. Thus, the paracrine/autocrine role of EV-associated fibronectin in systemic circulation to mediate fibroblast invasion during the pathogenesis of combined pulmonary fibrosis and emphysema, such as emphysema, smoking-related interstitial lung fibrosis/disease, IPF, and nonspecific interstitial pneumonia, is suggested. We also found that peptides/PSMs from gelsolin (GSN) were differentially enriched among smokers versus COPD comparisons. In this study, independent of the isolation method, we found two recently identified plasma-derived EV proteins CD5 antigen-like (CD5L) and galectin-3-binding protein (LGALS3BP)46 present in nonsmokers, smokers, and 10656


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Table 5. Clinical Characteristics of Human Subjects and Plasma-Derived EVs Used for Proteomics Analysisa groups

donor/subject

age (years)

sex (F/M)

smoking status (pack years)

FEV1 % predicted

nonsmokers

1* 2* 3* 4# 5# 6#

67 62 68 56 49 51 58.83 ± 8.08 48 45 63 52 ± 9.64 70 67 57 60 66 68 64.66 ± 5.04

F F F F F M

NS NS NS NS NS NS N/A current smoker (70) current smoker (25) current smoker (69) 54.66 ± 25.69 ex-smoker (52) current smoker (120) current smoker (46) current smoker (20) ex-smoker (25) ex-smoker (90) 58.83 ± 38.92

128% 118% 99% 96% 89% 87% 103% ± 0.16 93% 72% 97% 87% ± 0.13 49% 42% 38% 26% 47% 53% 43% ± 0.09

average (SD) smokers

average (SD) COPD

average (SD)

8* 9* 10* 11* 12* 13* 14# 15# 16#

F F M M F F F M M

a

Samples used in batch 1 (*) and batch 2 (#) donors/subjects are marked with the symbols. F: female; M: male; FEV1: Forced expiratory volume in one second; NS: nonsmokers.

EVs from smokers and patients with COPD compared to nonsmokers as new circulating EV biomarkers before validating them.

have been shown to be elevated during acute respiratory distress syndrome and acute lung injury specifically targeting cell types such as epithelial, endothelial, immune, smooth muscle, and fibroblast.60 A prior report has shown that enhanced levels of HA is found in lungs of patients with COPD, which is linked to decline in lung function and inflammation.61 Earlier, a genome-wide association study provided evidence that single-nucleotide polymorphisms in fibulin-3, an ECM-glycoprotein gene (EFEMP1), were associated with forced vital capacity.62 The two novel ECM proteins identified in EVs from COPD could be interesting targets that need to be tested in a relatively larger cohort of samples before we can classify them as new EV biomarkers for COPD. Future studies will be directed using fresh human serum or plasma along with novel in vitro (lung cell types) and in vivo models of chronic inflammatory lung diseases to identify novel EV biomarkers. This will expand our knowledge and understanding of EV/exosomal markers using proteomics approaches. We know that our study has several limitations, such as different isolation methods used for different experiments, making the comparison difficult. We compared ExoQuick- versus Exo-Spin-based EV preparations by using label-free LC−MS/MS analysis with slight modification. The steps involved in the isolation of EVs (ExoQuick vs Exo-Spin) may also have affected the subpopulation of exosomes versus MPs enriched in the preparations, which explains the differences observed in proteins detected (batch 1 vs batch 2). We combined batches 1 and 2 from MS analysis together after batch normalization. We had n = 3/group for ExoQuickbased isolation from the three groups (nonsmokers, smokers, and patients with COPD) and n = 3/group for Exo-Spin for only two groups (nonsmokers and patients with COPD). It would be ideal to increase the sample size, keep similar samples between groups and follow a single method of EV isolation and characterization including downstream processing for MS analysis to obtain statistically significant changes between groups. Therefore, we were unable to conclusively state that identified EV markers differentially enriched in plasma-derived

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CONCLUSIONS Overall, this pilot study demonstrates that methods of plasmaderived EV isolation play an important role in the outcomes of proteomics data analyses. We found that the majority of plasma-derived EVs were within the size range that contained enriched EV markers along with other phospholipid−protein lipoprotein complexes. It will be interesting to investigate if these lipoproteins are indeed packaged in the EVs/exosomes or if they are detected as contaminants present in EV preparations. It is possible that plasma proteins involved in immune modulation (complement activation), lipoproteins (apolipoproteins), and ECM proteins may be enriched inside EVs or present on the surface of EVs during altered tissue homeostasis in the target organs, thereby facilitating packaging of these proteins in EV cargos. Future studies will address the issues regarding isolation methods (EVs free from contaminating plasma proteins including lipoproteins) and high throughput quantitative mass spectrometry analysis of EV biomarkers in the normal versus diseased state that can be used for both prognosis, diagnosis, and therapeutics of chronic inflammatory lung diseases. Future studies will also be directed to correlate the smoking history, steroid resistance, and exacerbations with the decline in the lung function along with distinct EV markers in patients with COPD.

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MATERIALS AND METHODS Ethical Approval: Human Study Protocol and Institutional Biosafety Approvals. All protocols, procedures, and subject/patient recruitment described in this study were approved by the ethical Institutional Review Board (IRB)/ Research Subject Review Board (RSRB) committee of the University of Rochester Medical Center, Rochester, NY (RSRB#00028789). All experiments performed in this study were approved and in accordance with the University of Rochester Institutional Biosafety Committee. All subjects and 10657


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min. Finally, the grids were stained with 2% uranyl acetate and observed with Hitachi 7650 Analytical TEM. Nanoparticle Tracking Analysis (NanoSight NS300). Particle size and concentration of plasma-derived EVs were analyzed by NTA using the NanoSight Technology NS300. In brief, EVs isolated by ExoQuick/Exo-Spin were diluted in PBS (1:1000) and mixed before being introduced into the sample chamber using a syringe pump with a fixed flow rate. Three video recordings were made for a period of 60 s each. NS300 uses a combination of shutter speed and gain followed by manual focusing, which allows for optimum visualization of the maximum number of EVs. The NTA post-acquisition settings were optimized and kept constant between the samples and analyzed as described previously.65 TGX Stain-Free Gel. The TGX Stain-Free FastCast Acrylamide kit from Bio-Rad was used for preparing 7.5% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gel. This kit enables casting gels and running EV protein samples in SDS-PAGE gel as quickly as 30 min according to manufacturer’s instructions. Thus, it helps to visualize proteins in the gel before transfer or in the membrane after transfer. We used this method to determine the enrichment of plasma-derived EV proteins (25 μg/well) in isolated samples based on their MW/bands observed compared to protein MW standards (lane 1; Figure 3A). Slot-Blot Analysis for Exosomal Markers. EV protein concentrations were measured using the micro BCA kit. Twenty five micrograms of protein was diluted in 30 μL PBS; then, it was applied to a nitrocellulose membrane under gentle vacuum pressure using a slot-blot manifold (Harvard apparatus). The nitrocellulose membrane was blocked with 5% BSA or milk-containing tris-buffered saline with Tween 20 and subsequently incubated overnight at 4 °C with specific primary antibodies (1:1000 dilution) such as Rab-5b (Santa Cruz, sc-373725), TSG101 (Santa Cruz, sc-7964), and Alix (Santa Cruz, sc-53540). After three washing steps (10 min each), the levels of the protein were detected by probing with a specific secondary anti-mouse antibody (1:20 000 dilution) linked to horseradish peroxidase (Dako, Santa Barbara, CA, USA) for 1 h, and bound complexes were detected using the enhanced chemiluminescence method (PerkinElmer, Waltham, MA). Images were captured by Bio-Rad ChemiDoc MP, Imaging system. FluoroCet Quantification. EVs isolated from ExoQuick/ Exo-Spin methods were used for the FluoroCet exosome quantification kit (FluoroCet ultrasensitive exosome quantitation assay kit; System Biosciences, Palo Alto CA). In brief, plasma-derived EVs isolated from nonsmokers, smokers, and patients with COPD were lysed in the provided exosome lysis buffer, and AChE activity was quantified according to the manufacturer’s instructions. AChE activity in samples was determined by comparison with the standard curve that was calibrated to isolated EVs confirmed by NanoSight analysis provided in the kit. AChE activity was expressed as exosome abundance or average relative fluorescence units. Sample Preparation and Trypsin Digestion. We processed EV proteins from nonsmokers, smokers, and patients with COPD in two batches. In batch 1, we processed n = 3 per group isolated using the ExoQuick method; and in batch 2, we processed n = 3 per group (only for nonsmokers and COPD groups) isolated using the Exo-Spin method. The second batch samples were prepared identically as described below, except the IgG was depleted from the samples with a

patients provided written informed consent. Select plasma samples from nonsmokers, smokers, and patients with COPD from an earlier study were used.63 Human Subjects. Please refer to the clinical characteristics summary of subjects as described previously.63 Specifically, nonsmokers (n = 6, average age 58.83 with range 49−68 years), smokers (n = 3, average age 52 with range 45−63 years, 55 cigarettes pack years; current smokers), and COPD with current and ex-smokers (n = 6, average age 65 with range 57− 70 yrs, 58 cigarettes pack years) with their sexes, and FEV1 % predicted nonsmokers average 103% (range 87−128), smokers average 87 (range 72−97), and COPD average 43 (range 26− 53) were used in EV proteomics analysis of this study (Table 5). Collection of Human Blood/Plasma. Nonfasting blood samples (50−100 mL) were obtained from nonsmokers, smokers, and patients with COPD between 8 a.m. and 2 p.m. Blood samples were collected using the BD vacutainer brand Safety-Lok blood collection set using BD vacutainer K2 EDTA plus blood collection tubes. The anticoagulant-treated blood samples were mixed with equal volumes of sterile HBSS by inverting the tube several times. The diluted blood samples (20 mL) were each transferred to a clean 50 mL centrifuge tube containing Ficoll-Paque media (15 mL). The tubes were then centrifuged at 800 g for 15 min at 20 °C. The upper layer containing plasma was collected from each sample and stored in a 50 mL falcon tube at −80 °C until use. The layers of mononuclear cells (monocytes) were isolated, washed, and plated for another study. Isolation of Plasma-Derived EVs Using Kits. ExoQuick Method. ExoQuick exosome precipitation solution was obtained from System Biosciences and stored at 4 °C. Human plasma samples were thawed on ice for isolation of EVs using the ExoQuick method. In brief, plasma samples were centrifuged at 3000g for 15 min to remove cells and cell debris. Clarified plasma samples were then transferred to a clean tube containing an equal volume of ExoQuick exosome precipitation solution. These mixtures were incubated overnight at 4 °C and then centrifuged at 1500g for 30 min. The supernatant was aspirated, and the EV pellets were resuspended in a suitable buffer or lysis reagent for downstream applications. Exo-Spin Method. The Exo-Spin exosome purification kit for plasma was purchased from Cell Guidance Systems and stored at 4 °C. Human plasma samples were thawed on ice for isolation of EVs using the Exo-Spin method. In brief, 250−500 μL of plasma samples were centrifuged first at 300g and then at 16 000g for 10 and 30 min, respectively, to remove platelets and larger vesicles. Half the volume of Exo-Spin buffer was added to the plasma samples, which were then mixed by inverting and incubated at 4 °C for at least 1 h before centrifugation at 16 000g for 60 min. The EV pellets were resuspended in 100−200 μL phosphate-buffered saline (PBS) and purified using the Exo-Spin column. Finally, EVs were eluted in 100−200 μL PBS for downstream processing/ application. Transmission Electron Microscopy. EVs were visualized using TEM as described previously.64 Briefly, 2−4 μL of EVs were fixed in 4% paraformaldehyde and deposited onto carbon-coated electron microscopy grids. Then, the grids were washed twice with 1× PBS, followed by 1× PBS containing glycine (50 mM) each for 3 min, and then with 1× PBS containing bovine serum albumin (BSA) (0.5%) for 10 10658


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slurry of protein G sepharose fast flow prior to running the samples on the gel. The EVs were lysed using radioimmunoprecipitation assay buffer containing protease inhibitors and run on SDS-PAGE gel until they were about 1 cm into the gel. Then, the gel was stained using SimplyBlue SafeStain and washed overnight in water. The next day, the entire gel plug containing the EV proteins was excised and cut into small pieces, and then washed with ammonium bicarbonate (ABC) buffer (100 mM). The samples were destained with 50% acetonitrile in ABC buffer, followed by 100% acetonitrile to completely dehydrate the gel pieces. Dithiothreitol was added to reduce the proteins, followed by iodoacetamide to alkylate the proteins. After washing the gel pieces with ABC and 50% acetonitrile in ABC, the gel samples were completely dehydrated with 100% acetonitrile. Trypsin was added to the gel pieces, and the samples were incubated overnight at 37 °C. The following day, the peptides were extracted twice with 50% acetonitrile, 0.1% trifluoroacetic acid (TFA), once with 100% acetonitrile, and the extracts were combined into a single tube. The samples were dried down in a SpeedVac and then desalted with Pierce 100 μL C18 Tips. Label-Free Quantification of EV/Exosomal Proteins by LC−MS/MS. The dried peptides were reconstituted in 40 μL of 0.1% TFA in water and placed into an autosampler vial. One microliter of the sample was injected onto a 30 cm × 100 μm C18 column, packed with Sepax 1.8 μm beads, using an EASY nLC-1000 UPLC. The solvents were A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile), with a flow rate of 300 nL/min throughout the run. The gradient began at 3% B and was increased to 30% B over 94 min, increasing to 50% B over 5 min, and then to 70% B in 4 min, and was held steady at 70% B for 5 min. The gradient went back to starting conditions in 4 min and was held there for 8 min to re-equilibrate the column. The total acquisition time was 120 min. A Q Exactive Plus mass spectrometer was used to analyze the EV protein samples. A full scan with a mass range of 400− 1400 m/z was taken, followed by the MS/MS scans of the top 10 most abundant peaks, after which the fragmented peak would go onto an exclusion list for 25 s. The MS1 resolution was 70 000 at m/z = 200, with a maximum ion injection time of 50 ms and an AGC setting of 1 × 106. The MS2 resolution was 17 500 at m/z = 200, with a maximum injection time of 57 ms and an AGC setting of 5 × 104. Batch 2 samples followed an identical process, except that the method was shortened to 60 min after it was determined that there were less than 1000 proteins identified in each sample, and the injection volume was increased to 2 μL/sample. Proteomics Data Analysis. Raw data were searched within Proteome Discoverer 1.4 using the Mascot search engine against the human SwissProt database. The percolator was used as the FDR calculator, which was set to filter out peptides with a q value greater than 0.01. The precursor ions area detector node was used to determine the peak area of each peptide, wherever possible. The area reported at the protein level was the average area of the three most abundant peptides of that protein. Raw data for batches 1 and 2 will be provided upon written request after publication of this study and will be deposited online using the data submission guidelines for EV proteomics data sets. EV/Exosome Database Search and Online Tools. The identified peptides and PSMs from human plasma-derived EVs

protein names or gene names were searched using online databases such as Vesiclepedia (http://microvesicles.org/),66 ExoCarta (http://exocarta.org/),67 EVpedia (http://evpedia. info/),68 and PPD (http://www.plasmaproteomedatabase.org/ ).69 In brief, these curated databases provide a compendium of proteins identified in several plasma-derived EV preparations. We performed a PPI network analysis of enriched peptides or PSMs identified in EV preparations from this study, using the STRING functional annotation protein interaction database (https://string-db.org/).70 Information from all the abovementioned exosome/EV databases was accessed in November 2018. Venn diagrams were created using the online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to compare lists of proteins (peptides/PSMs) to find common and unique EV/exosome markers identified in this study compared with curated plasma-derived EV databases. We used ClustVis, a web tool for visualizing clustering of multivariate data, for generating the heatmap and hierarchical clustering analyses.71 Statistical Analysis. To examine the nonsmokers, and patients with COPD group differences in peptides and PSMs, a general linear model was used to fit the proteomic data after adjusting the batch effect. Linear contrasts were conducted for pairwise comparisons. The raw p-values were adjusted using the Benjamini−Hochberg procedure to control the FDR at 5%. The limma package in Bioconductor/R was used for fitting the general linear models with the Empirical Bayes approach.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.9b00966. GO enrichment analysis for EV markers identified in this study that are common among top 100 EV/exosomal markers present in Vesiclepedia/ExoCarta/PPD analyzed by the STRING database; summary of significant peptide and PSM counts analyzed by pairwise comparison; GO enrichment analysis for peptides identified in this study that are significantly different among nonsmokers versus smokers and smokers versus COPD analyzed by the STRING database; identified EV markers based on database searches from top 100 Vesiclepedia, ExoCarta, EVpedia, and PPD; representative sample of plasma-derived EVs isolated by the ExoSpin method was split into two to compare before and after depletion of IgG; hierarchical clustering analyses of overall plasma-derived EV peptide and PSM counts including significant peptide counts based on pairwise comparisons; STRING PPI network using the proteins identified in this study versus top 100 EV markers from PPD; and full blot images used for slot-blot analysis of plasma-derived EV markers (ZIP)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 1 585 273 3034. Fax: 1 585 276 0239. ORCID

Isaac K. Sundar: 0000-0001-6742-3460 10659


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Author Contributions

(14) Jimenez, J. J.; Jy, W.; Mauro, L. M.; et al. Endothelial cells release phenotypically and quantitatively distinct microparticles in activation and apoptosis. Thromb. Res. 2003, 109, 175−180. (15) Escola, J.-M.; Kleijmeer, M. J.; Stoorvogel, W.; et al. Selective enrichment of tetraspan proteins on the internal vesicles of multivesicular endosomes and on exosomes secreted by human Blymphocytes. J. Biol. Chem. 1998, 273, 20121−20127. (16) Yoshioka, Y.; Konishi, Y.; Kosaka, N.; et al. Comparative marker analysis of extracellular vesicles in different human cancer types. J. Extracell. Vesicles 2013, 2, 20424. (17) Kesimer, M.; Gupta, R. Physical characterization and profiling of airway epithelial derived exosomes using light scattering. Methods 2015, 87, 59−63. (18) Bhatnagar, S.; Shinagawa, K.; Castellino, F. J.; et al. Exosomes released from macrophages infected with intracellular pathogens stimulate a proinflammatory response in vitro and in vivo. Blood 2007, 110, 3234−3244. (19) Bourdonnay, E.; Zasłona, Z.; Penke, L. R. K.; et al. Transcellular delivery of vesicular SOCS proteins from macrophages to epithelial cells blunts inflammatory signaling. J. Exp. Med. 2015, 212, 729−742. (20) Harischandra, D. S.; Ghaisas, S.; Rokad, D.; et al. Exosomes in Toxicology: Relevance to Chemical Exposure and Pathogenesis of Environmentally Linked Diseases. Toxicol. Sci. 2017, 158, 3−13. (21) Hough, K. P.; Chanda, D.; Duncan, S. R.; et al. Exosomes in immunoregulation of chronic lung diseases. Allergy 2017, 72, 534− 544. (22) Benedikter, B. J.; Wouters, E. F. M.; Savelkoul, P. H. M.; et al. Extracellular vesicles released in response to respiratory exposures: implications for chronic disease. J. Toxicol. Environ. Health B Crit. Rev. 2018, 21, 142−160. (23) Fujita, Y.; Araya, J.; Ito, S.; et al. Suppression of autophagy by extracellular vesicles promotes myofibroblast differentiation in COPD pathogenesis. J. Extracell. Vesicles 2015, 4, 28388. (24) Yáñez-Mó, M.; Siljander, PR; Andreu, Z.; et al. Biological properties of extracellular vesicles and their physiological functions. J. Extracell. Vesicles 2015, 4, 27066. (25) Henson, P. M.; Vandivier, R. W.; Douglas, I. S. Cell death, remodeling, and repair in chronic obstructive pulmonary disease? Proc. Am. Thorac. Soc. 2006, 3, 713−717. (26) Schmidt, E. P.; Tuder, R. M. Role of Apoptosis in Amplifying Inflammatory Responses in Lung Diseases. J. Cell Death 2010, 3, JCD.S5375. (27) Kasahara, Y.; Tuder, R. M.; Cool, C. D.; et al. Endothelial cell death and decreased expression of vascular endothelial growth factor and vascular endothelial growth factor receptor 2 in emphysema. Am. J. Respir. Crit. Care Med. 2001, 163, 737−744. (28) Gordon, C.; Gudi, K.; Krause, A.; et al. Circulating endothelial microparticles as a measure of early lung destruction in cigarette smokers. Am. J. Respir. Crit. Care Med. 2011, 184, 224−232. (29) Thomashow, M. A.; Shimbo, D.; Parikh, M. A.; et al. Endothelial microparticles in mild chronic obstructive pulmonary disease and emphysema. The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease study. Am. J. Respir. Crit. Care Med. 2013, 188, 60−68. (30) Tsuji, T.; Aoshiba, K.; Nagai, A. Alveolar cell senescence in patients with pulmonary emphysema. Am. J. Respir. Crit. Care Med. 2006, 174, 886−893. (31) Tsuji, T.; Aoshiba, K.; Nagai, A. Alveolar cell senescence exacerbates pulmonary inflammation in patients with chronic obstructive pulmonary disease. Respiration 2010, 80, 59−70. (32) Takahashi, T.; Kubo, H. The role of microparticles in chronic obstructive pulmonary disease. Int. J. Chronic Obstruct. Pulm. Dis. 2014, 9, 303−314. (33) Pisetsky, D. S.; Gauley, J.; Ullal, A. J. HMGB1 and microparticles as mediators of the immune response to cell death. Antioxid. Redox Signaling 2011, 15, 2209−2219.

I.K.S. and I.R. conceived and designed the experiments; I.K.S. conducted the experiments; I.K.S. and D.L. performed data analysis; I.K.S. wrote the initial draft of the manuscript; I.K.S., D.L., and I.R. edited the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported in part by the University of Rochester’s Lung Biology Strategic Plan Pilot Project (I.K.S.) and the National Institute of Health NIH 2R01HL085613, HL137738, HL135613, and ES028006 (I.R.). Dr. Li’s effort is in part supported by the University of Rochester CTSA award number UL1 TR002001 from the National Center for Advancing Translational Sciences of the National Institutes of Health. Authors would like to thank Krishna P. Maremanda, PhD and Samantha R. McDonough, BS for their help in editing and formatting references.

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REFERENCES

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