Secretory vesicles are neutrophil intracellular storage granules formed by endocytosis. Understanding the functional consequences of secretory vesicle exocytosis requires knowledge of their membrane proteins. The current study was designed to use proteomic technologies to develop a more complete catalog of secretory vesicle membrane proteins and to compare the proteomes of secretory vesicle and plasma membranes. A total of 1118 proteins were identified, 573 (51%) were present only in plasma membrane-enriched fractions, 418 (37%) only in secretory vesicle-enriched membrane fractions, and 127 (11%) in both fractions. Gene Ontology categorized 373 of these proteins as integral membrane proteins. Proteins typically associated with other intracellular organelles, including nuclei, mitochondria, and ribosomes, were identified in both membrane fractions. Ingenuity Pathway Knowledge Base analysis determined that the majority of canonical and functional pathways were significantly associated with proteins from both plasma membrane-enriched and secretory vesicle-enriched fractions. There were, however, some canonical signaling pathways that involved proteins only from plasma membranes or secretory vesicles. In conclusion, a number of proteins were identified that may elucidate mechanisms and functional consequences of secretory vesicle exocytosis. The small number of common proteins suggests that the hypothesis that secretory vesicles are formed from plasma membranes by endocytosis requires more critical evaluation.

Circulating neutrophils are capable of undergoing a series of phenotypic changes that result in their transition from cells that are poorly responsive to proinflammatory stimuli to become the primary effector cells of innate immunity. These phenotypic changes involve the incorporation of proteins from the membranes of intracellular storage granules into the plasma membrane and the release of proteins stored in granule matrix through regulated exocytosis. Granule exocytosis contributes to enhanced neutrophil tethering and adhesion to vascular endothelial cells at a site of inflammation; enhanced migration across blood vessel walls; chemotaxis to a site of microbial invasion; phagocytosis of invading organisms; and microbicidal activity through a combination of enzymatic degradation, reactive oxygen species generation, and release of microbicidal peptides into phagosomes. Neutrophils contain a heterologous group of storage granules that have been classified into four subsets based on density and composition: azurophil (primary) granules, specific (secondary) granules, gelatinase (tertiary) granules, and secretory vesicles (1). These granule subsets undergo hierarchical stimulated exocytosis, with secretory vesicles the most easily and completely mobilized (2, 3, 4). Gelatinase, specific, and azurophil granules are formed from the trans-Golgi network during neutrophil maturation (1). The intragranule constituents of secretory vesicles include plasma proteins, resulting in the hypothesis that secretory vesicles are formed by endocytosis and that functional changes from their exocytosis are due entirely to incorporation of new molecules into the plasma membrane (1, 5). Thus, to understand the changes in neutrophil functional capability induced by secretory vesicle exocytosis, a comprehensive catalog of membrane proteins is required.

Proteomic techniques, which include methods for protein extraction and separation, protein identification and characterization, and database analysis, provide an unbiased approach to identifying proteins expressed in subcellular compartments (6, 7, 8, 9). We recently published a comprehensive proteomic analysis of neutrophil gelatinase, specific, and azurophil granules (10). Using protein separation by two-dimensional gel electrophoresis and two-dimensional HPLC coupled with MALDI-TOF-MS3 and ESI-MS/MS, 286 proteins were identified on one or more granule subsets, many of which had not been found previously on neutrophil granules. The current study was designed to use similar proteomic technologies to provide a more complete identification of secretory vesicle membrane proteins and to compare those proteins with the proteins expressed on neutrophil plasma membranes. The ability to extract and solubilize membrane proteins is a major limitation to all proteomic approaches. To overcome this limitation, proteins were extracted from membranes using a recently described methanol extraction procedure, followed by two-dimensional HPLC and ESI-MS/MS (11). With this approach, we identified a number of membrane spanning and membrane associated proteins and uncovered significant differences between secretory vesicle-enriched and plasma membrane-enriched proteomes.

Neutrophils were isolated from healthy donors using plasma-Percoll gradients as described by Haslett et al. (12). Trypan blue staining revealed that at least 97% of cells were neutrophils with >95% viability. After isolation, neutrophils were suspended in Krebs-Ringer phosphate buffer (pH 7.2) at 4 × 107 cells/ml and treated with 5 mM diisopropyl fluorophosphate for 10 min on ice to inhibit proteases (13). Extensive evaluation using respiratory burst activity and granule exocytosis indicates that this isolation technique does not prime neutrophils. The Human Studies Committee of the University of Louisville approved the use of human donors.

Neutrophil plasma membrane and secretory vesicle membranes were enriched by centrifugation on a two-layer Percoll density gradient as described by Dahlgren et al. (14). Briefly, isolated neutrophils from single donors (4 × 107/ml) were incubated with diisopropyl fluorophosphate, pelleted by centrifugation, and resuspended in disruption buffer containing 100 mM KCl, 1 mM NaCl, 1 mM ATPNa2, 3.5 mM MgCl2, 10 mM PIPES, and 0.5 mM PMSF. Cells were disrupted by nitrogen cavitation at 380 p.s.i. and 4°C. The cavitate was collected, supplemented with 1.5 mM EGTA, and nuclei and intact cells were removed by centrifugation at 400 × g for 5 min. The supernatant membrane suspension was aspirated, placed in a 50-ml conical centrifuge tube, and mixed with an equal volume of a 1.12 g/ml Percoll gradient. A total of 10 ml of the membrane suspension/Percoll gradient was layered under 5 ml of disruption buffer in a 50-ml ultracentrifuge tube. A total of 10 ml of 1.05 g/ml Percoll gradient solution was layered under the membrane suspension, then 5 ml of 1.12 g/ml Percoll gradient solution layered under the 1.05 g/ml solution. The gradient was centrifuged at 37,000 × g in a SS-34 fixed angle rotor in a Sorvall RC-5B centrifuge for 30 min at 4°C. Following centrifugation, successive 1.5-ml fractions were collected from the top of the gradient.

Fractions obtained from Percoll gradients were analyzed for alkaline phosphatase in the absence (nonlatent) or presence (latent) of Triton X-100 using p-nitrophenylphosphate as substrate. Briefly, 100:l aliquots of each fraction were added to wells of a 96 flat-well plate with or without 0.3% Triton X-100. Reactions were started by adding 200 μl of reaction buffer containing 5 mM p-nitrophenylphosphate in 100 mM 2-amino-2-methyl-1-propanol (pH 10.0). Following incubation for 30 min at 37°C, reactions were stopped by addition of 150 μl of 0.04 N NaOH and absorbance was read at 405 nm in a microplate reader. Fractions containing nonlatent, but not latent, alkaline phosphatase were pooled and analyzed as plasma membrane-enriched. Fractions containing latent alkaline phosphatase were pooled and analyzed as secretory vesicle-enriched membrane. Percoll was removed from membranes by ultracentrifugation at 100,000 × g for 90 min. Membrane pellets were resuspended in 50 mM ammonium bicarbonate, washed by centrifugation at 100,000 × g, then resuspended in 60% methanol in 100 mM ammonium bicarbonate to extract membrane proteins (11). Proteins were digested by incubation with 3 μg each of trypsin and chymotrypsin at 37°C overnight. Following centrifugation at 100,000 × g for 20 min at 4°C, the supernatant was removed for peptide analysis. Peptides were lyophilized and prepared for mass spectrometry analysis using a desalting trap method. Briefly, peptides were resuspended in a 100 μl of 5% acetonitrile and 0.05% formic acid and applied to a peptide microtrap (Michrom BioResources) equilibrated with 1 ml of the same buffer. The trap was then washed twice with 100 μl of resuspension buffer and peptides eluted with 100 μl of 95% acetonitrile, 0.05% formic acid. Eluted peptides were dried in a speed vacuum and resuspended in 5–10 μl of 5% acetonitrile and 0.05% formic acid.

A modified version of a previously described 2D-LC-MS/MS method was applied (15). Trypsin/Chymotrypsin-generated peptides were loaded onto an analytical 2D microcapillary chromatography column packed with 3–4 cm of 5 μm (pore size) strong cation exchange (SCX) resin (Phenomenex) followed by 2–3 cm of 5 μm (pore size) C18 reversed-phase (RP) resin (Phenomenex). This bi-phasic column was attached to an analytical RP chromatography column (100 × 365 μm fused silica capillary with an integrated, laser pulled emitter tip packed with 10 cm of Synergi 4 μm RP80A (Phenomenex)). Peptides were eluted from SCX with seven step gradients of 5, 10, 15, 30, 50, 70, and 100% 500 mM ammonium acetate. Following each SCX elution step, peptides were ionized and sprayed into the mass spectrometer using the following linear RP gradient: 20 min: 0% B, 80 min: 40% B, 90 min: and 60% B at a flow rate of 200 nl/min (mobile phase A: 5% acetonitrile/0.1% formic acid and mobile phase B: 80% acetonitrile/0.1% formic acid). Spectra were acquired with a LTQ linear ion trap mass spectrometer (Thermo Fisher Scientific). During LC-MS/MS analysis, the mass spectrometer performed data-dependent acquisition with a full MS scan between 300 and 2000 mass to change ratio followed by five MS/MS scans (35% collision energy) on the five most intense ions from the preceding MS scan. Data acquisition was performed using dynamic exclusion with a repeat count of 30 and a 1 and 3 min exclusion duration window.

The acquired mass spectrometric data were searched against a human protein database (human RefSeq) using the Sequest algorithm and a commercial computational platform (SequestSorcerer, Sage-N Research) assuming modifications of oxidation of methionine (+15.99) and carbamidomethylation of cysteine (+57). High-probability protein identifications were assigned from the Sequest results using the BIGCAT (16) and ProteinProphet (17) statistical platforms (18, 19). Both of these programs eliminate redundant listing by grouping proteins with 100% identity and merging proteins with 100% shared MS/MS spectra. The BIGCAT filter uses Sequest Xcorr cut-offs of 1.5, 2, and 2.5 for +1, +2, and +3 ions, respectively. Proteins were also ranked by relative abundance or enrichment using a protein abundance factor (PAF). The PAF is defined as the total number of nonredundant spectra (spectral counts) that correlate significantly to each respective candidate protein normalized to the protein’s m.w. (×104). Studies demonstrating linearity between the number of spectral counts and protein concentration provide the framework for this type of label-free quantitative analysis from 2D-LC-MS/MS experiments (20, 21, 22). The PAF approach has been highly successful in the development of statistical models based on 2D-LC-MS/MS experimental data (23, 24, 25). ProteinProphet gives each protein a ranked probability score, with 1.0 being the highest probability. Our results were fitted into a model where probability scores decreasing from 1.0 were correlated to a predicted false positive identification error rate. Proteins with a probability score greater than 0.65 predicted a false positive error of <10%, and those proteins were included in the list of candidates.

The proteins were further analyzed using the National Center for Biotechnology Information (NCBI) database. Any proteins that were removed from the database in the process of annotation or that were identified as a bacterial protein were excluded from the final protein list. To identify integral membrane proteins and to determine intracellular location and function, identified proteins were analyzed using the NCBI database, by Gene Ontology (26), and by the Ingenuity Pathways Knowledge Base (IPKB) (Ingenuity Systems) (27). The IPBK is a comprehensive knowledge base of biological findings for genes of human, mouse, and rat origin, which is used to construct pathways and define biological functions (28). The canonical pathways are well-characterized metabolic and cell signaling pathways that have been curated from specific journal articles, review articles, text books, and KEGG Ligand. The functional analysis has three primary categories of functions: molecular and cellular functions; physiological system development and function; and diseases and disorders. There are 85 high-level functional categories that are classified under these categories. The significance value of a given canonical pathway or functional analysis category is a measurement of the likelihood that the pathway or function is associated with the data set by random chance. The value of p is calculated using the right-tailed Fisher Exact Test, and values of p < 0.05 were a priori assumed to be statistically significant.

Neutrophils from four individual donors were subjected to membrane fractionation, and each set was analyzed separately by mass spectrometry. A total of 1118 proteins were identified, for which there was a <10% probability that the identification was by chance according to Protein Prophet, and which were confirmed to have mammalian homologues according to the NCBI database. Of the 1118 proteins, 573 were present only in plasma membrane-enriched fractions, 418 only in secretory vesicle-enriched fractions, and 127 in both fractions. The 1118 proteins, together with their probability score, the peptides used to identify them, the membrane fraction from which they were identified, and the Gene Ontology analysis of membrane association, cellular location, and function, are listed in supplemental data Tables I and II.4

Table I.

Distribution of proteins by membrane association and cell locationa

LocationMembrane-AssociatedMembrane-IndependentMembrane Association UnknownTotal
Cytoplasm 151 152 
Cytoskeleton 65 66 
Endoplasmic reticulum 27 13 44 
Endosome/lysosome/peroxisome 16 
Extracellular 44 44 
Golgi 13 
Mitochondria 24 31 59 
Nucleus 196 213 
Plasma membrane 268 268 
Ribosome 25 25 
Secretory granule 16 17 34 
Unknown 14 170 184 
Total 373 552 193 1118 
LocationMembrane-AssociatedMembrane-IndependentMembrane Association UnknownTotal
Cytoplasm 151 152 
Cytoskeleton 65 66 
Endoplasmic reticulum 27 13 44 
Endosome/lysosome/peroxisome 16 
Extracellular 44 44 
Golgi 13 
Mitochondria 24 31 59 
Nucleus 196 213 
Plasma membrane 268 268 
Ribosome 25 25 
Secretory granule 16 17 34 
Unknown 14 170 184 
Total 373 552 193 1118 
a

All identified proteins were categorized by Gene Ontology as integral membrane proteins (membrane-associated), as not associated with membranes (membrane-independent), and proteins whose membrane association was unknown and then listed by cellular location.

Table II.

Distribution of proteins by cell locationa

LocationPlasma MembraneSecretory VesicleCommon to PM and SVTotal
Cytoplasm 70 68 14 152 
Cytoskeleton 30 22 14 66 
Endoplasmic reticulum 25 10 44 
Endosome/lysosome/peroxisome 16 
Extracellular 23 16 44 
Golgi 13 
Mitochondria 33 15 11 59 
Nucleus 113 81 19 213 
Plasma membrane 159 76 33 268 
Ribosome 22 25 
Secretory granule 10 15 34 
Unknown 100 78 184 
Total 573 418 127 1118 
LocationPlasma MembraneSecretory VesicleCommon to PM and SVTotal
Cytoplasm 70 68 14 152 
Cytoskeleton 30 22 14 66 
Endoplasmic reticulum 25 10 44 
Endosome/lysosome/peroxisome 16 
Extracellular 23 16 44 
Golgi 13 
Mitochondria 33 15 11 59 
Nucleus 113 81 19 213 
Plasma membrane 159 76 33 268 
Ribosome 22 25 
Secretory granule 10 15 34 
Unknown 100 78 184 
Total 573 418 127 1118 
a

All proteins identified were categorized by the membrane fraction from which they were identified, plasma membrane (PM), secretory vesicle (SV) membrane, or present in both membrane fractions (common to PM and SV). These proteins were then listed by cell location based on Gene Ontology analysis.

In the past, protein extraction methods used in proteomic analysis have provided poor identification of transmembrane proteins. To address this limitation, we used a recently described protein extraction method in which 60% methanol was added to 100 mM ammonium bicarbonate, as recently described by Fischer et al. (11). To determine the effectiveness of methanol extraction in the recovery of membrane proteins, each protein was analyzed for known association with cellular membranes using the Gene Ontology database. Of the 1118 proteins identified, 373 were categorized as integral membrane proteins, 552 were not associated with any cellular membranes, and the membrane association of 193 proteins was unknown (Table I). A number of proteins with single transmembrane spanning regions were identified, including FcγRIIa and IIIa, integrins α2b, α3, α4, αD, αM, αX, β1, and β2; TLRs 2 and 8; and complement receptors 1 and 2. Numerous proteins with multiple transmembrane spanning regions were also identified, including seven transmembrane spanning receptors for leukotiene B4, formyl peptides, IL-8, C5a, and a number of ion channels and transporters. Table I also shows the cellular location of proteins that were integral to membranes, independent of membranes, or where membrane association was unknown. As expected, all 268 plasma membrane proteins were categorized as membrane-associated. The remainder of the membrane-associated proteins were primarily localized to the endoplasmic reticulum (27), mitochondria (24), and secretory granules (16). The majority of proteins not associated with a cellular membrane were localized in the cytoplasm (151), nucleus (196), or with the cytoskeleton (65). The majority of proteins for which membrane association was unknown also had an unknown cellular location (169 of 193 proteins).

Table II shows the cellular location of proteins identified in plasma membrane-enriched and secretory vesicle-enriched fractions. Based on the Gene Ontology database, 25% of proteins were predicted to be from mitochondria, ribosomes, or nuclei. Of the remaining 821 proteins, over half were proteins known to be associated with the cytoskeleton or a cellular membrane compartment, including plasma membrane, secretory granules, endoplasmic reticulum, endosomes, or golgi. Thus, the fractions enriched for plasma membrane and secretory vesicle membranes also contain proteins from a number of other cellular compartments. Another recent analysis reported that proteins from endoplasmic reticulum, mitochondria, and golgi coisolated with neutrophil secretory vesicle-enriched membranes and enriched plasma membranes separated by free flow electrophoresis (29). Of the 1118 proteins identified, 51% were found in plasma membrane-enriched fractions, 37% in secretory vesicle-enriched fractions, and 11% were found in both fractions. When proteins were analyzed according to their cellular location, several patterns were observed. Proteins localized to the endoplasmic reticulum, extracellular space, mitochondria, nucleus, plasma membrane, and whose location was unknown were more likely to be present in plasma membrane-enriched fractions. Proteins localized from endosome/lysosome/peroxisome compartments, golgi, ribosomes, and secretory granules were more likely to be present in secretory vesicle-enriched fractions. Proteins defined as cytoplasmic or cytoskeletal were fairly equally distributed between plasma membrane-enriched and secretory vesicle-enriched fractions. Surprisingly, the number of proteins present in both fractions was low for all Gene Ontology cell locations, ranging from 3% for proteins of unknown location to 26% for proteins in secretory granules. These results indicate that there was effective separation of cellular membranes based on their density, and the data suggest that proteins from all cellular compartments segregate into distinct membrane compartments of different densities.

To obtain a more accurate assessment of the functions of proteins most likely associated with plasma membrane and secretory vesicle, proteins localized to mitochondria, nuclei, and ribosomes were eliminated from the analysis. All proteins identified from plasma membrane-enriched fractions and secretory vesicle-enriched fractions, however, are listed in supplementary Table II. Table III shows the distribution of the remaining 821 proteins from plasma membrane-enriched and secretory vesicle-enriched fractions according to protein function. Functional categories were assigned based on analysis by the Gene Ontology database. Where multiple functions were assigned to a protein, the function most likely to be pertinent to neutrophils was chosen. The majority of proteins were classified into the following functional categories: adhesion, cytoskeletal regulation, enzyme/metabolism, kinase/phosphatase, membrane trafficking, proteolysis, receptor/signal transduction, and transport. For a given functional class, plasma membrane-enriched fractions and secretory vesicle-enriched fractions contained either an approximately equal number of proteins or there were more proteins in the plasma membrane-enriched fractions. Only a limited number of proteins in each functional category were found in both fractions, ranging from 6% of transport proteins to 22% of cytoskeletal regulatory proteins.

Table III.

Distribution of proteins by functional categorya

FunctionPlasma MembraneSecretory VesicleCommon to PM and SVTotal
Adhesion 31 14 51 
Chaperone/protein folding 17 
Chromosome organization 
Cytoskeletal regulation 29 27 16 72 
Enzyme/metabolism 28 23 59 
GTPase 13 20 38 
Immune/inflammatory response 17 
Kinase/phosphatase 33 15 49 
Membrane trafficking 30 22 57 
Protein Modification/targeting 10 
Proteolysis 23 22 50 
Receptor/signal transduction 49 27 83 
Redox 
Structural proteins 11 
Translation/transcription 19 
Transport 33 16 52 
Miscellaneous 12 11 28 
Unknown 108 79 11 198 
Total 426 300 95 821 
FunctionPlasma MembraneSecretory VesicleCommon to PM and SVTotal
Adhesion 31 14 51 
Chaperone/protein folding 17 
Chromosome organization 
Cytoskeletal regulation 29 27 16 72 
Enzyme/metabolism 28 23 59 
GTPase 13 20 38 
Immune/inflammatory response 17 
Kinase/phosphatase 33 15 49 
Membrane trafficking 30 22 57 
Protein Modification/targeting 10 
Proteolysis 23 22 50 
Receptor/signal transduction 49 27 83 
Redox 
Structural proteins 11 
Translation/transcription 19 
Transport 33 16 52 
Miscellaneous 12 11 28 
Unknown 108 79 11 198 
Total 426 300 95 821 
a

Proteins, excluding those identified as nuclear, mitochondrial, or ribosomal, were categorized by the membrane fraction from which they were identified, plasma membrane (PM), secretory vesicle (SV) membrane, or present in both membrane fractions (common to PM and SV). These proteins were then listed by cellular function based on Gene Ontology analysis.

In addition to identification of 57 proteins involved in membrane trafficking, a total of 38 GTPases or their regulatory proteins were identified. Thus, 95 proteins were identified that are recognized to participate in regulation of events leading to membrane trafficking. These proteins were equally distributed between plasma membrane-enriched and secretory vesicle-enriched fractions, and only 10 proteins were present in both fractions (Rap1B, Rab1B, Rab5C, Rab7, Gαi2, dynein 8, kinesin 27, reticulon 3c, testilin, and secretory carrier membrane protein 2).

To better understand the functional roles of plasma membrane and secretory vesicle proteins, the 821 proteins, excluding those localized to mitochondria, nuclei, and ribosomes, were subjected to IPKB analysis for canonical pathways and functional pathways. Canonical pathways are defined as well-characterized metabolic and cell signaling pathways, whereas functional pathways contain three primary categories of functions: molecular and cellular functions; physiological system development and function; and diseases and disorders. Of the 821 proteins, 743 could be mapped using the IPKB, 368 proteins from the plasma membrane-enriched fractions, 282 proteins from the secretory vesicle-enriched fractions, and 93 protein identified in both fractions. Table IV lists the functional pathways that demonstrated a significant association with proteins identified in each membrane fraction. The vast majority of functions, including cell signaling; cellular movement; hematologic, infectious, immunologic, and inflammatory diseases; and molecular transport, were significantly associated with proteins in all three groups, plasma membrane-enriched fractions, secretory vesicle-enriched fractions, and proteins common to both fractions. Thus, proteins performing these functions were not segregated into any particular membrane fraction. Table V lists the canonical pathways identified by the presence of multiple proteins from each pathway in the membrane fraction. Three patterns were observed. The largest number of canonical pathways contained proteins present in both plasma membrane-enriched and secretory vesicle-enriched fractions, and/or proteins which were common to both membrane fractions. This group of pathways included actin cytoskeletal signaling, integrin signaling, leukocyte extravasation signaling, protein ubiquitination, oxidative stress, ERK or JNK signaling, and growth factor (platelet-derived growth factor and epidermal growth factor) signaling. Canonical pathways containing proteins only identified in secretory vesicle-enriched fractions included G-protein coupled receptor signaling, fibroblast growth factor signaling, PI3K/AKT signaling, and FcεR signaling. Canonical pathways containing proteins only identified in plasma membrane-enriched fractions included phosphatase and tensin homolog signaling, TLR signaling, TGF-β signaling, and NF-κB signaling. Thus, plasma membrane-enriched and secretory vesicle-enriched fractions contained proteins common to a number of functions and signaling pathways. There were, however, protein components of signaling pathways unique to plasma membrane and to secretory vesicles.

Table IV.

Pattern of functions of plasma membrane and secretory vesicle proteinsa

Secretory Vesicle Proteins OnlySecretory Vesicle and Common ProteinsPlasma Membrane, Secretory Vesicle, and Common Proteins
Gene expression Protein degradation Cell signaling Carbohydrate metabolism 
Protein folding Protein trafficking Cellular movement Cell death 
 Free radical scavenging Cell morphology Molecular transport 
 Nucleic acid metabolism Tissue development Immune system function 
  Cellular development Immunologic disease 
  Cell growth and proliferation Post-translational modification 
  Infectious disease Mineral metabolism 
  Immune response Lipid metabolism 
  Cellular assembly and organization Metabolic disease 
  Cell-to-cell signaling and interaction Hematological disease 
  Inflammatory disease  
Secretory Vesicle Proteins OnlySecretory Vesicle and Common ProteinsPlasma Membrane, Secretory Vesicle, and Common Proteins
Gene expression Protein degradation Cell signaling Carbohydrate metabolism 
Protein folding Protein trafficking Cellular movement Cell death 
 Free radical scavenging Cell morphology Molecular transport 
 Nucleic acid metabolism Tissue development Immune system function 
  Cellular development Immunologic disease 
  Cell growth and proliferation Post-translational modification 
  Infectious disease Mineral metabolism 
  Immune response Lipid metabolism 
  Cellular assembly and organization Metabolic disease 
  Cell-to-cell signaling and interaction Hematological disease 
  Inflammatory disease  
a

Proteins, excluding those identified as nuclear, mitochondrial, or ribosomal, were categorized by the membrane fraction from which they were identified, plasma membrane (PM), secretory vesicle (SV) membrane, or present in both membrane fractions (common to PM and SV). Each of these categories was analyzed by Ingenuity Pathway Knowledge Base to determine the likelihood that functional pathways were associated with the dataset by random chance. Only those functional pathways significantly associated with proteins from one or more membrane fractions are listed.

Table V.

Pattern of proteins in canonical pathwaysa

PM and SV Proteins and/or Common ProteinsSV ProteinsPM Proteins
Actin cytoskeleton signaling cAMP-mediated signaling Phosphatase and tensin homolog signaling 
Integrin signaling G-protein coupled receptor signaling Apoptosis signaling 
Leukocyte extravasation signaling Fibroblast growth factor signaling NK cell signaling 
Calcium signaling PI3K/AKT signaling TLR signaling 
Complement and coagulation cascade Inositol metabolism WNT/β-catenin signaling 
Vascular endothelial growth factor signaling FcεR signaling TGF-β signaling 
Ag presentation pathway  NF-κB signaling 
Protein ubiquitination pathway   
Oxidative stress response   
Epidermal growth factor signaling   
Platelet-derived growth factor signaling   
ERK/MAPK signaling   
JNK signaling   
PM and SV Proteins and/or Common ProteinsSV ProteinsPM Proteins
Actin cytoskeleton signaling cAMP-mediated signaling Phosphatase and tensin homolog signaling 
Integrin signaling G-protein coupled receptor signaling Apoptosis signaling 
Leukocyte extravasation signaling Fibroblast growth factor signaling NK cell signaling 
Calcium signaling PI3K/AKT signaling TLR signaling 
Complement and coagulation cascade Inositol metabolism WNT/β-catenin signaling 
Vascular endothelial growth factor signaling FcεR signaling TGF-β signaling 
Ag presentation pathway  NF-κB signaling 
Protein ubiquitination pathway   
Oxidative stress response   
Epidermal growth factor signaling   
Platelet-derived growth factor signaling   
ERK/MAPK signaling   
JNK signaling   
a

Proteins, excluding those identified as nuclear, mitochondrial, or ribosomal, were categorized by the membrane fraction from which they were identified, plasma membrane (PM), secretory vesicle (SV) membrane, or present in both membrane fractions (common to PM and SV). Each of these categories was analyzed by Ingenuity Pathway Knowledge Base to determine the likelihood that canonical pathways were associated with the dataset by random chance. Only those canonical pathways significantly associated with proteins from one or more membrane fractions are listed.

Understanding the functional consequences of secretory vesicle exocytosis in neutrophils requires knowledge of the membrane proteins of this organelle. Previous studies using primarily Ab-based methods identified individual proteins from secretory vesicle membranes, including cytochrome b558 oxidase, CD11b/CD18 adhesion molecules, complement receptor 1 (CR1 or CD35), formyl peptide receptors, Fc(RIIIa) (CD16), membrane metalloendopeptidase (CD10), aminopeptidase N (CD13), the TLR complex molecule CD14, the transmembrane protein tyrosine phosphatase CD45, V-type H+-ATPase, the soluble NSF attachment receptor (SNARE) protein VAMP-2, and the metalloproteinase leukolysin (1). Subcellular fractionation, combined with mass spectrometry-based proteomics, represents a powerful approach to unbiased identification of the protein composition of intracellular organelles (6, 7, 8, 9). The current study applied these techniques to develop a more complete catalog of neutrophil secretory vesicle membrane proteins and to compare the proteomes of secretory vesicle membranes and plasma membranes. Over 1100 proteins were identified, the majority of which segregated to either the plasma membrane-enriched fractions or the secretory vesicle-enriched membrane fractions; only 11% of identified proteins were present in both membrane fractions.

Two significant problems affect the interpretation of our data. First, the sensitivity of proteomic analysis makes the ability to obtain highly purified intracellular organelles the limiting factor in establishing the proteome of a specific organelle. Based on the classification of our proteins by cell location and function using Gene Ontology, proteins typically associated with other intracellular organelles, notably nuclei, mitochondria, and ribosomes, were identified in both secretory vesicle-enriched and plasma membrane-enriched fractions. One reason for the presence of those contaminating proteins is that nitrogen cavitation partially disrupts a number of intracellular compartments, including mitochondria, golgi, endoplasmic reticulum, and nuclei. In the case of neutrophils, this disruption is reported to extend to intracellular storage granules (10, 29, 30, 31). Soluble proteins released from these granules likely associate and cosediment with membranes from granule-free fractions, consistent with our finding myeloperoxidase, elastase, and lactoferrin in plasma membrane and secretory vesicle membrane fractions. It is also likely that membranes released by partial disruption of other organelles cosediment in plasma membrane-enriched and secretory vesicle-enriched membrane fractions. Jethwaney et al. (29) identified proteins derived from mitochondria and endoplasmic reticulum in secretory vesicle-enriched fractions derived by free-flow electrophoresis, whereas proteins from these and other organelles were distributed in both membrane fractions in the current study. Whether the presence in both membrane fractions of proteins from a broader range of organelles in our study reflects differences in membrane enrichment or protein identification techniques between the two studies cannot be determined. No direct comparison of free-flow electrophoresis and density-gradient centrifugation enrichment of secretory vesicle-enriched and plasma membrane-enriched fractions has been performed. Although the presence of latent alkaline phosphatase activity indicates that both methods obtain fractions containing secretory vesicles, a comparative study is needed to determine similarities and differences between the two methods.

A total of 73 proteins were assigned by Gene Ontology to endosomes, golgi, or endoplasmic reticulum. The possible localization of those proteins to plasma membranes or secretory vesicles, rather than other intracellular membrane compartments, was not examined further in this study. A recent report, in which proteomic analysis of phagosomes was performed, determined that endoplasmic reticulum fuse with maturing phagosomes in macrophages, suggesting one mechanism by which proteins may be “shared” by different intracellular compartments (9). Thus, it is likely that some proteins that localize to cellular components, such as endoplasmic reticulum or endosomes, may also be associated with secretory vesicles or plasma membrane.

The second problem with application of proteomic techniques to identification of proteins in secretory and plasma membranes is the difficulty extracting and identifying transmembrane proteins containing α helices. Identifying transmembrane proteins is made difficult by the absence of sites for tryptic cleavage in transmembrane regions, variability in the size of exposed hydrophobic regions, low abundance of transmembrane proteins, poor separation by two-dimensional gel electrophoresis of integral membrane proteins, and poor solubility of hydrophobic peptides. Fischer et al. (11) recently reported that tryptic digestion of bacterial membrane proteins extracted in 60% methanol increased identification of integral membrane proteins from 20 to 50% of the total proteins identified. Our results suggest that this approach is useful in membranes from mammalian cells, as one-third of the proteins identified in the present study were classified as integral membrane proteins by Gene Ontology.

The observation that only 11% of proteins identified were common to both plasma membrane-enriched and secretory vesicle-enriched fractions suggests that Percoll density-gradient centrifugation effectively enriched two different membrane populations. Combined with the assays for total and latent alkaline phosphatase, our findings suggest that there are greater differences in the protein content of secretory vesicles and plasma membrane than previously appreciated. Jethwaney et al. (29) used free-flow electrophoresis to separate plasma membrane from secretory vesicles, separated proteins by SDS-PAGE, and identified proteins using HPLC-MS/MS. Similar to our results, these authors identified 30 proteins present in the plasma membrane-enriched fractions and 36 proteins in secretory vesicle-enriched fractions, whereas only 7 proteins (10%) were common to both fractions. Several explanations were considered to account for the low percentage of proteins that were common to both membrane fractions. First, secretory vesicles are not endocytic vesicles derived from the plasma membrane, as has been previously postulated (1, 5). Second, the protein extraction and/or identification techniques failed to identify proteins common to the two membranes. Third, separation of plasma membrane and secretory vesicle membrane by density results in identification of proteins that segregate according to membrane density, rather than by organelle. It is unlikely that the sensitivity of proteomic techniques would vary between the two membrane fractions, resulting in the failure to identify proteins common to both. The localization of latent alkaline phosphatase to the membrane fraction with higher density suggests that proteins associated with secretory vesicles were limited to one group of membrane fractions. Our data do not allow us to distinguish between the possibility that secretory vesicles are not endocytic vesicles or that all membranes contain domains of lighter and heavier density to which distinct sets of proteins localize. However, the data suggest that the hypothesis that secretory vesicles are formed from plasma membranes by endocytosis requires more critical evaluation.

The cell functional classification based on the Gene Ontology database revealed a number of proteins that may elucidate mechanisms of secretory vesicle exocytosis. Examination of the 38 GTPases, the majority of which were identified in secretory vesicle-enriched fractions, revealed a number of Rab proteins known to regulate membrane trafficking. Secretory vesicle-enriched fractions contained Rab11a, Rab14, Rab15, and Rab35; plasma membrane-enriched fractions contained Rab5b and Rab31, and Rab1b, Rab5c, and Rab7 were common to both plasma membrane and secretory vesicle-enriched fractions. Rab5 was reported to play a significant role in chemoattractant receptor endocytosis and fusion of intracellular granules with phagosomes in human neutrophils (32, 33, 34). Rab5a was shown to undergo a significant translocation from endosomes and secretory vesicles to the plasma membrane with stimulation of human neutrophils (35), and Rab11 was reported to regulate endocytic-exocytic cycling of integrin molecules (36). The roles of Rab 14, Rab15, Rab31, and Rab35 have not been examined in neutrophils. A total of 57 proteins were identified that play a role in membrane trafficking, 30 were in the plasma membrane, 22 in secretory vesicles, and 5 were common to both. The present study found two SNARE proteins, VAMP-3 and VAMP-8, on secretory vesicle-enriched membranes. VAMP-1, −2, −7, and −8 were reported previously to be present in human neutrophils, and VAMP-2 was identified on secretory vesicles (37, 38, 39, 40). VAMP-3 has not been found previously in human neutrophils, although it has been identified in human platelets and human plasma cells (41, 42). Mollinedo et al. (39, 40) reported that VAMP-1 and VAMP-2 mediated exocytosis of specific granules, whereas VAMP-1 and VAMP-7 mediated azurophil granule exocytosis. The SNARE proteins involved in secretory vesicle exocytosis have not been determined. In addition to SNARE proteins, the exocyst complex is a group of eight proteins involved in vesicle targeting and docking at the plasma membrane (43, 44). Two components of the exocyst complex were identified in the current study: exocyst complex component 2 (Sec3) was identified from plasma membrane-enriched fractions, and exocyst complex component 5 (Sec10) was identified from secretory vesicle-enriched fractions. Boyd et al. (45) reported that Sec3p localized to the plasma membrane of Saccharomyces cerevisiae, while Sec10p was found on vesicles. The exocyst complex is an effector for multiple GTPases, including cdc42 and Rab11 (46, 47). Both of these GTPases were identified, cdc42 from plasma membrane-enriched fractions and Rab11A from secretory vesicle-enriched fractions. These findings suggest that the exocyst complex plays a role in tethering secretory vesicles to the plasma membrane before SNARE-mediated membrane fusion. Consistent with our previous studies which showed large amounts of actin associated with both neutrophil plasma membranes and secretory vesicle membranes (4), 72 cytoskeletal and cytoskeletal regulatory and binding proteins were equally distributed in both membrane fractions. Two groups of actin assembly factors were identified. Formins act in conjunction with profilin to drive nucleation, but not branching, of actin filaments (48). Formin 1 and 2 were identified from plasma membrane-enriched fractions, and profilin 1 was common to both fractions. The Arp2/3 complex acts as a nucleation and branching factor, and members of this complex were identified from secretory vesicle-enriched fractions. Although it was suggested previously that actin and actin-associated proteins reflect cytoskeletal contamination of these membrane fractions (29), the high content of cytoskeletal proteins and the importance of actin reorganization in exocytosis of secretory granules make it more likely that cytoskeletal proteins are associated with plasma and secretory vesicle membranes (4, 10).

The authors have no financial conflict of interest.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1

This work was supported by a Merit Review Grant from the Department of Veterans Affairs (to K.R.M.), National Institutes of Health Grants DK62389 (to R.A.W. and K.R.M.) and DK176743 (to D.W.P.), and the Office of Science Financial Assistance Program, Department of Energy (to D.W.P.).

3

Abbreviations used in this paper: MS, mass spectrometry; SCX, strong cation exchange; RP, reversed-phase; PAF, protein abundance factor; NCBI, National Center for Biotechnology Information; IPKB, Ingenuity Pathways Knowledge Base; SNARE, soluble NSF attachment receptor.

4

The online version of this article contains supplemental material.

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