Proteomic Profiling of Endothelial Cells in Human Lung Cancer Hye-Jeong Park,† Byung-Gyu Kim,† Seung-Jin Lee,† Sun-Hee Heo,† Jae-Young Kim,† Tae-Hwan Kwon,‡ Eung-Bae Lee,§ Hyun-Mo Ryoo,| and Je-Yoel Cho*,† Department of Biochemistry, School of Dentistry, Kyungpook National University, Daegu, Korea, Department of Biochemistry and Cell Biology, School of Medicine, Kyungpook National University, Daegu, Korea, Department of Thoracic and Cardiovascular Surgery, School of Medicine, Kyungpook National University Hospital, Daegu, Korea, and Department of Pharmacology and Dental Therapeutics, School of Dentistry, Seoul National University, Seoul, Korea Received November 9, 2007
Genomic and proteomic analysis of normal and diseased tissues have yielded an abundance of molecular information for diagnostic and potential therapeutic targets. Changing the target of analysis from poorly accessible cells within tissues to easily accessible vascular endothelium has theoretical advantages in tissue-specific targeting. In this study, we sought to map a large-scale proteome of microvascular endothelium in human non-small cell lung cancer (NSCLC) and normal lung tissues, and identify lung cancer-related endothelial cell (EC)-selective proteins. Endothelial cells were isolated within NSCLC tissues and adjacent-normal lung tissue of lung cancer patients by using CD31immunomagnetic beads. The complex proteins from the ECs were separated by one-dimensional gel electrophoresis, and the proteins in each gel band were digested by trypsin. Peptides were separated by online reverse-phase liquid-chromatography and analyzed by electrospray ionization (ESI) ion trap tandem mass spectrometry. Approximately 600-1000 proteins were identified in each individual sample. Five patient cases of paired individual data, extracted from the protein identification data sets of both normal- and cancer-derived ECs, were analyzed by subtractive proteomics. An average of 300 proteins was specifically identified from each lung cancer-derived EC isolate, compared to normal lung-derived ECs. With the use of several comparative analyses, we identified among those 300 proteins, 16 common candidate proteins that were detected in at least 3 of 5 cases specific to lung cancer-derived ECs. Proteins selectively identified in cancer-derived ECs, including coatomer protein complex, subunit gamma (COPG), and peroxiredoxin 4 (PRDX4), were validated by Western blot analysis. In an additional experiment in which 16 cancer samples were analyzed by immunohistochemistry, PRDX4, thymopoietin (TMPO), and COPG were confirmed to be abundantly expressed in lung cancer-derived ECs and in cancerous lung cells. Further ongoing analysis of these 16 candidate proteins will determine their potential applicability to NSCLC-specific diagnosis and therapeutics. Keywords: Endothelial cells • Non-small cell lung carcinoma • GeLC-MS/MS
Introduction Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC), consisting mainly of adenocarcinoma, squamous cell, and large-cell carcinoma, accounts for almost 80% of lung cancer cases, and most patients with NSCLC exhibit an advanced stage of * To whom correspondence should be addressed. Je-Yoel Cho, Ph.D., Department of Biochemistry, School of Dentistry, Kyungpook National University, 101 Dongin-dong 2-ga, Daegu, 700-422, Korea. Phone: +82-53420-4997. Fax: +82-53-421-1417. E-mail:
[email protected]. † Department of Biochemistry, School of Dentistry, Kyungpook National University. ‡ Department of Biochemistry and Cell Biology, School of Medicine, Kyungpook National University. § Department of Thoracic and Cardiovascular Surgery, School of Medicine, Kyungpook National University Hospital. | Department of Pharmacology and Dental Therapeutics, School of Dentistry, Seoul National University.
1138 Journal of Proteome Research 2008, 7, 1138–1150 Published on Web 01/26/2008
disease.1 Regardless of the subtype, the 5-year survival rate for lung cancer is the lowest at 10–15%, when compared to other common cancers. Surgical resection is the mainstay of treatment for early stage patients, but disease diagnosis is usually late and prognosis is poor.1,2 Therefore, there is a great need to identify effective biomarkers with a systemic relevance to lung cancer. Such molecular tools could possibly lead to early detection of disease and an increased survival rate among patients. Recently, there have been various efforts using “omics” technologies, like genomics and proteomics, to improve diagnostic classification and identify specific genes or proteins potentially suitable as molecular targets for improved diagnosis and therapy.3 However, these approaches have produced an overabundance of molecular information for diagnosis and potential therapeutic targets, and have presented difficulties in sorting out useful candidates from this massive body of 10.1021/pr7007237 CCC: $40.75
2008 American Chemical Society
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Lung Cancer-Derived Endothelial Cell Proteome Table 1. Characteristics of Patients with NSCLC case
age
sex
diagnosis
Case 1
66
Male
Adenocarcinoma
Case 2
66
Male
Squamous cell carcinoma
Case 3
52
Female
Bronchioloaveolar carcinoma
Case 4
53
Male
Adenocarcinoma
Case 5
72
Male
Adenocarcinoma
stagea
isolated ECs/mL
T2N0M0, stage IB T1N0M0, stage IA T2N0M0, stage IB T4N2M0, stage IIIB T2N0M0, stage IB
N : 3 × 10 , Cc: 6 × 105 N: 4.3 × 105, C: 2.3 × 105 N: 2 × 105, C: 1.5 × 105 N: 5.7 × 105, C: 3 × 105 N: 6.1 × 105, C: 6.5 × 105 b
5
notes
Visceral pleural invasion Visceral pleural invasion Satellite nodule, malignant pleural effusion Visceral pleural invasion
a Tumor stage; T, tumor size or depth; N, lymph node spread; M, presence or absence of metastasis; stage, disease stage.47 ECs. c C, lung cancer-derived ECs.
information.4–6 Various attempts are being made to reduce data complexity to a manageable level, such as focusing on subcellular fractionation, post-translational modification, and tissue fractionation. Also, there is another desired goal of discovering cancer-specific biomarkers by selectively targeting a single organ or diseased tissue in vivo, and overcoming in vivo barriers to effectively deliver drugs, imaging agents, or genes to specific tissues in sufficiently effective quantities. Endothelium and/or epithelium can act as initial barriers, limiting the accessibility of many drugs and agents to target sites of pharmacological action.7–9 Thus, new approaches are needed to reduce the tissue data complexity and overcome inaccessibility of diseased tissue for discovery and validation of accessible tissue-specific targets. In this report, we focused attention on protein markers or targets of the vascular endothelium, the interface between the circulating blood and the underlying cells inside the tissue, rather than on poorly accessible and complex cells within tissues. The appeal of endothelium as a biomarker is its molecular heterogeneity, which can be induced by its surrounding microenvironment, not only in different tissues, but also in normal versus diseased tissues.10–14 Many studies have identified cancer-specific endothelial markers13,14 and cancerstage or type-specific endothelial molecules.15 There are reports of mapping endothelial cell surfaces to elucidate molecular differences between rat lungs in vivo and in vitro12 and of proteomic technologies used to determine difference between rat lungs and tumors.13 However, cancer EC-specific molecules have not been well-investigated in human NSCLC because of technical limitations in molecular profiling of endothelium, which comprises a low percentage of the total cells within a given tissue. In this study, we sought to create a large-scale proteome map of microvascular endothelium in human NSCLC tissues and adjacent-normal lung tissues, and have found lung cancer ECselective candidate proteins.
Experimental Section Materials. CD31-immunomagnetic beads and a magnetic separation device (magnetic particle concentrator) were purchased from Dynal (Oslo, Norway). A glass grinder was purchased from Kontes (Vineland, NJ). A cell strainer was obtained from BD Falcon (Bedford, MA). Premixed stacking gel solutions (4% and 5%) and premixed running gel solutions (10% and 12%) were purchased from Elpis-Biotech (Korea). A Bradford protein assay kit and Coomassie G250 stain were obtained from Bio-Rad (Hercules, CA). Trypsin, modified sequencing-
b
N, normal lung-derived
grade, and complete protease inhibitor cocktail tablets were purchased from Roche (Mammheim, Germany). Ammonium bicarbonate and acetonitrile were obtained from Sigma-Aldrich (St. Louis, MO). Formic acid was purchased from Fluka (Switzerland). β-Actin antibody and TMPO antibody were purchased from Abcam (Cambridge, MA). PRDX4 antibody for Western blot analysis and COPG antibody were purchased from Santa Cruz Biotechnology (Santa Cruz, CA). PRDX4 antibody for immunohistochemisty was purchased from Biomol (Plymouth Meeting, PA). eNOS antibody was obtained from BD Biosciences (San Jose, CA). Human Tissue Specimen and Sample Preparation. NSCLC tissues and adjacent-normal lung tissues were obtained during cancer surgery from 8 patients at Kyungpook National University Hospital. Additionally, 16 NSCLC tissues and pairednormal lung tissues were obtained from the archives of the Department of Pathology at the Kyungpook National University Hospital. All samples were obtained after receiving patients’ agreements and institutional permission with genome research permission No. KNUH 2006–20. Pathological diagnosis of cancer tissues was determined by a pathologist in the hospital’s clinical pathology department. Each cancer stage was evaluated and described by the International System for Staging Lung Cancer adopted by the American Joint Committee on Cancer (AJCC) and the Union Internationale Contre le Cancer (UICC), as shown in Table 1. Fresh NSCLC tissues and paired canceradjacent normal lung tissues were immersed in PBS on ice immediately after surgery, and then transferred to a laboratory within 30 min. Collected tissues (about 1 g each) were held on ice and minced into fine pieces using a surgical blade, and washed with 0.1% BSA/PBS to remove excrescent tissues such as fat and blood. Prior to homogenization, each piece was carefully examined to determine whether the tissue contained necrosis or extra fatty composition. Minced tissues were ground to make single cells using a glass grinder on ice, and then ground tissues were filtered using cell strainers to eliminate tissue debris. Filtered, ground tissues were washed repeatedly by resuspension and centrifugation at 700 rpm for 5 min at 4 °C, and the cell pellets were finally resuspended in 0.1% BSA/ PBS. Endothelial Cell Enrichment and Cell Lysis. CD31-immunomagnetic beads were added to each tissue cell suspension in an Eppendorf tube and were incubated at 4 °C for 40 min, with gentle tilting and rotation. After incubation, endothelial cells were separated from the complex single cell suspensions by placing the Epppendorf tube in the magnetic separation device for 1 min and removing the supernatant very carefully Journal of Proteome Research • Vol. 7, No. 3, 2008 1139
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Figure 1. Verification of EC enrichment from lung tissues. (A) Representative images of human lung tissues, surgically resected NSCLC (lower) and adjacent-normal lung tissue (upper). (B) Single cells separated from lung tissue. Minced tissues were ground to detach complex cells comprising lung tissue. (C) CD31immunomagnetic beads bound to ECs. Rosetted ECs were separated from single cell tissue suspension and attracted by an immunomagnet. (D) Western blot analysis of proteins from lung tissues (TCL) and enriched ECs (HLMVEC) using the antibodies as indicated. TCL, total lung tissue lysate; HLMVEC, enriched human lung microvascular endothelial cells; HUVEC, human umbilical vascular endothelial cells (positive control); L132, human embryonic lung epithelial cell line (negative control); eNOS, endothelial nitric oxide synthase (EC- specific marker); β -Actin (sample loading control).
from the side opposite of the beads. The pellet was resuspended by gentle pipetting in a volume of 0.1% BSA/PBS equal to the volume discarded. Isolated ECs were counted using a hematocytometer under a microscope. EC samples which yielded a concentration of g1.5 × 105 cells/mL were used to acquire an amount of protein sufficient to perform proteomic analysis and a least one Western blot analysis confirmation experiment. These isolated ECs were lysed in a lysis buffer (50 mM TrisHCl, pH7.4, 150 mM NaCl, 1 mM EDTA, and 1% Triton X-100) with protease inhibitor cocktail, incubated for 20 min at 4 °C, and centrifuged for 20 min at 12 000 rpm at 4 °C. The concentration of total proteins in the supernatant was assayed using the protein assay kit and BSA as a standard. In-Gel Tryptic Digestion. One-dimensional gel electrophoresis and Coomassie staining were performed as previously reported.16 An amount of 30 µg of isolated protein from ECs was loaded in each lane for SDS-PAGE. In-gel digestion was performed according to methods reported previously.16 Briefly, excised gel slices were washed with 0.1 M ammonium bicarbonate and 50% (v/v) acetonitrile in 0.1 M ammonium bicarbonate until residual SDS and Coomassie blue dye were completely removed. Destained gel slices were dehydrated in acetonitrile and dried in a vacuum centrifuge. Proteins within each dehydrated gel slice were then digested overnight with trypsin at a substrate/enzyme ratio of 10:1 (w/w) in 25 mM ammonium bicarbonate, pH 8.0. The enzyme reaction was terminated by addition of 0.1% formic acid in water. Peptides were extracted by sonication for 10 min, and supernatants containing peptides were transferred to new tubes. Nano-LC-ESI-MS/MS Analysis. A 2D-LC-MS/MS system with reverse-phase liquid chromatography (RP-LC) was used for the proteome analysis. This system is composed of a Surveyor MS pump (Thermo Electron, San Jose, CA), a Spark auto sampler (EMMEN, The Netherlands), and a Finnigan LTQ 1140
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Park et al. linear ion trap mass spectrometer (Thermo Electron, San Jose, CA) equipped with nanospray ionization (NSI) sources. Tryptic digested peptides in 12 µL of 0.1% formic acid were injected for LC-MS/MS analyses. Samples were first injected into a peptide CapTrap cartridge for peptide concentration and desalting, and then applied to a reverse-phase (RP) column that had been packed in-house with 5 µm, 300 Å pore size C18 silica gel, for peptides separation according to protein hydrophobicity on a RP column. The RP column was installed directly in front of an ion transfer tube for electrospray. Solutions used as mobile phases were A (H2O) and B (acetonitrile, ACN), and both solutions contained 0.1% (v/v) formic acid. The flow rate was maintained at 200 nL/min. The solvent gradient was started at 5% B, and a linear gradient to 65% B was achieved in 43 min. The gradient was then ramped to 80% B in 7 min, and then to 100% A in the next 10 min. The mass spectrometer was operated in a data-dependent mode (m/z 300–1800) in which each full MS scan was followed by five MS/MS scans. For each MS/MS scan, the five most abundant peptide molecular ions were dynamically selected from the prior scan for collision-induced dissociation (CID) using a normalized collision energy of 35%. The temperature of the heated capillary and the electrospray voltage were 195 °C and 2.0 kV, respectively. Bioinformatic Analysis. MS/MS spectra collected from 122 nano-LC-MS/MS runs were searched against an integrated and nonredundant protein database, the human International Protein Index (IPI) database (Version 3.05 that consists of 49161 protein entries) using the SEQUEST algorithm (Thermo Electron, San Jose, CA) incorporated in BioWorks software (version 3.2). Since redundancy in protein identification against database is problematic, we used the IPI database which was generated by ruling out redundant protein data sets.17 The database searches allowed for modification of oxidation on the methionine residue (16 Da), peptide mass tolerance of 2 atomic mass units, and fragment mass tolerance of 1 atomic mass unit. The database search was limited to peptides that could be generated only by tryptic cleavage. SEQUEST results were filtered by XCorr versus charge state, Delta Cn, and Rsp score. XCorr values of 1.9, 2.2, and 3.75 were used for matching with single, double, and triple charged ions, respectively. Settings of Delta Cn g 0.1 and Rsp e 4 were used. The protein confidence threshold cutoff for this study was established using a probability score with at least one peptide of e1.0 × 10-3 (99.9%). Proteins identified based on a single peptide with a SEQUEST score under 10 were eliminated in the process of selecting commonly expressed proteins in five paired data sets. These proteins were consistent with other independent purifications of endothelial cells from normal and lung cancer tissues followed by the GeLC-MS/MS procedure using LTQ Ion trap mass spectrometer. We carefully inspected presented data to determine whether identical peptide sequences were included in multiple unique protein sequences in the data sets. We also confirmed that a single protein from a protein group was singled out. After identifying proteins, subtractive proteomics was carried out for each data set using ProtAn, an inhouse analytic program. Western Blot Analysis. Western blot analysis was performed as previously reported.16 Briefly, 20 µg of protein isolated from ECs was loaded in each lane for SDS-PAGE. After SDS-PAGE, the transferred membrane was incubated with COPG antibody (Santa Cruz, CA) and PRDX4 antibody (Santa Cruz, CA) each diluted 1:1000, followed by horseradish peroxidase-conjugated anti-goat IgG secondary antibodies (1:2000 dilution). Signals
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Figure 2. EC proteome analysis in cancer- and normal-derived human lung tissues. (A) Total number of identified proteins from five cancer-dervied and paired-normal lung tissue EC samples after SEQUEST filtering criteria. Raw data obtained from GeLC-MS/MS for peptide identification were filtered according to criteria such as XCorr, Rsp, delta Cn, and probability. (B) Comparison of proteins identified in each paired sample (normal versus cancer). The number of proteins obtained from individual comparison in five normaland cancer-derived EC data sets. Within the boxes, encircled numbers represent the number of proteins identified in normal (right) or cancer (left) samples as follows: upper circles, proteins initially detected; middle circle, proteins common to normal and cancer; lower circles, proteins differentially expressed. (C), The procedure of combined analysis to sort normal (left) or cancer-specific proteins (right) detected in at least 3 of 5 case data sets.
were developed with ECL-PLUS detection reagent, and the membranes were then exposed to X-ray film for an appropriate time and developed. Immunohistochemistry. Paraffin-embedded tissue blocks from 16 NSCLC and paired-normal lung tissues were obtained from the archives of the Department of Pathology, as described above. Sections (5 µm) were dewaxed in xylene and rehydrated through graded ethanol, before endogenous peroxidase activity was quenched with 3% hydrogen peroxide in methanol for 10 min. Sections were boiled in a pressure cooker with citrate buffer for 14 min, and nonspecific binding was blocked with blocking solution for 10 min at room temperature. Sections were incubated overnight at 4 °C with diluted primary antibodies: PRDX4, 1:1000; TMPO, 1:500; and COPG, 1:100. Immunohistochemical staining was performed with the Histostain-Plus Bulk Kit (Zymed Laboratories Inc., South San Francisco, CA), and the chromogen used was 3,3′ diaminobenzidin (DAB) (Zymed Laboratories, Inc.). Methyl green was used for counterstaining, and after dehydration, coverslips were mounted with hydrophobic medium. Immunostaining results were evaluated semiquantitatively by dividing the staining reaction into five categories: -, no-immunostaining present; +, weak cytoplasmic staining; ++, moderate cytoplasmic staining; +++, strong cytoplasmic staining; ++++, strongest cytoplasmic staining. Also, nuclear staining was similarly assessed in normal and cancer cells. Samples were examined by two investigators,
who were blinded to the information of sections during microscopic examination and evaluation.
Results Isolation of Endothelial Cells from Human Lung Tissues. To isolate human microvascular endothelial cells from lung tissues, we used an immunomagnetic isolation method by means of magnetic beads coated with antibodies of CD31. We isolated an average of 4 × 105 rosetted endothelial cells from about 1–1.5 g of each NSCLC tissue (Table 1) and the adjacentnormal lung tissues (Figure 1A-C). The purity of ECs was assessed by Western blot analysis with antibodies for known EC-specific protein, endothelial nitric oxide synthase (eNOS), and epithelial marker, E-cadherin (E-cad). As shown in Figure 1D, eNOS was detected strongly in isolated human lung microvascular endothelial cells (HLMVECs), relative to that of total cell lysate (TCL), and E-cad was not detected in isolated HLMVECs. This demonstrates that ECs were efficiently enriched in the HLMVECs from lung tissues. Endothelial Cell Proteome Analysis in Human NSCLC and Adjacent Lung Tissues. To comprehensively analyze EC proteome in human NSCLC and normal lung tissue, we extracted endothelial total proteins from five NSCLC, four lung adenocarcinoma, and one squamous carcinoma, and from the paired adjacent-normal lung tissues. The protein complexes Journal of Proteome Research • Vol. 7, No. 3, 2008 1141
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Table 2. Partial List of Proteins Specifically Identified in Lung Cancer-Derived ECs functional class IPI accession number
1. Biosynthesis IPI00101782.2 IPI00013452.8 IPI00396485.2
protein name
specific function
localization
Biosynthesis Protein biosynthesis Protein biosynthesis
Cytoplasm Cytoplasm Cytoplasm
IPI00178440.2 IPI00216587.6 IPI00012493.1
GDP-mannose pyrophosphorylase A Glutamyl-prolyl tRNA synthetase Eukaryotic translation elongation factor 1 alpha 1 Elongation factor 1-beta Ribosomal protein S8 40S ribosomal protein S20
Protein biosynthesis Protein biosynthesis Protein biosynthesis
Cytoplasm Ribosome Ribosome
2. Catabolism IPI00220665.4
Hexokinase 1
Glycolysis
Alpha-L-fucosidase Isoform long of ubiquitin carboxyl-terminal hydrolase 5 Ubiquitin-conjugating enzyme E2N (UBC13 homologue, yeast)
Glycosaminoglycan catabolism Ubiquitin cycle
Mitochondrial outer membrane Lysosome Lysosome
Ubiquitin cycle
Proteasome
Heterogeneous nuclear ribonucleoprotein F APEX nuclease (multifunctional DNA repair enzyme) 1 Phosphoglucomutase 2 Chitinase domain containing 1 Pyridoxal (pyridoxine, vitamin B6) kinase Isoform 1 of polyadenylate-binding protein 1 Serpin peptidase inhibitor, clade H (heat shock protein 47), member 1, (collagen binding protein 1) NAD-dependent malic enzyme, mitochondrial precursor ATP-dependent DNA helicase 2 subunit 2 Thymopoietin isoform beta Splicing factor 3 subunit 1
RNA processing
Nucleus
Base-excision repair
Nucleus
Carbohydrate metabolism Carbohydrate metabolism Metabolism
Cytoplasm Extracellular region Cytoplasm
mRNA stabilization
Cytoplasm
Protein folding
Endoplasmic reticulum
Pyruvate metabolism
Mitochondrial matrix
Regulation of DNA repair
Nucleus
Regulation of transcription RNA processing
Nucleus
Niemann-Pick disease, type C1 Transmembrane EMP24 domain-containing protein 2 precursor Integrin, beta 6
Intracellular protein transport Intracellular protein transport
Integral to membrane Golgi apparatus
Cholesterol transport Electron transport
IPI00032297.2 IPI00026530.4
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5, 13 kDa Isovaleryl Coenzyme A dehydrogenase ERGIC-53 protein precursor
IPI00215918.2
ADP-ribosylation factor 4
IPI00001890.6
Coatomer protein complex, subunit gamma karyopherin (importin) beta 1 Coatomer subunit alpha
ER to Golgi vesicle-mediated transport/Small GTPase mediated signal transduction Intra-Golgi transport
Integral to plasma membrane Mitochondrial inner membrane Mitochondrial matrix ER-Golgi intermediate compartment Golgi apparatus
IPI00299026.4 IPI00024664.1 IPI00003949.1 3. Metabolism IPI00003881.4 IPI00215911.2 IPI00549837.2 IPI00045536.1 IPI00013004.1 IPI00008524.1 IPI00019880.1
IPI00011201.1 IPI00220834.7 IPI00030131.2 IPI00017451.1 4. Transport IPI00005107.1 IPI00016608.1
IPI00000151.1 IPI00412545.4
IPI00001639.2 IPI00295857.6 IPI00479905.4 IPI00291467.6 IPI00290032.1
1142
NADH dehydrogenase (ubiquinone) 1 beta subcomplex subunit 10 ADP/ATP Translocase 3 Transient receptor potential cation channel subfamily M member 7
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Electron transport ER to Golgi vesicle-mediated transport
Protein import into nucleus, docking Retrograde vesicle-mediated transport, Golgi to ER Electron transport Mitochondrial transport/ Apoptosis Ion transport/ Protein amino acid phosphorylation
Golgi apparatus Cytoplasm/Nucleus ER/Golgi Mitochondrial inner membrane Mitochondrial inner membrane Integral to membrane
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Lung Cancer-Derived Endothelial Cell Proteome Table 2. Continued functional class IPI accession number
protein name
specific function
localization
5. Cell Communication IPI00004315.1 Sialic acid binding Ig-like lectin 9
Integral to plasma membrane
IPI00155168.2
Integral to plasma membrane
IPI00008494.4 IPI00329213.1 IPI00007061.1 IPI00296563.3 IPI00020418.1 IPI00017592.1
IPI00444788.1
Cell surface receptor linked signal transduction Protein tyrosine phosphatase, receptor Cell surface receptor linked signal type, C transduction Intercellular adhesion molecule 1 Cell-cell adhesion (CD54), human rhinovirus receptor Inositol polyphosphate-5-phosphatase, Negative regulation of signal 145 kDa transduction Vesicle transport protein GOT1B Positive regulation of I-kappaB kinase/NF-kappaB cascade GUF1 GTPase homologue small GTPase mediated signal transduction Ras-related protein R-Ras Ras protein signal transduction Leucine zipper-EF-hand-containing Signal transduction transmembrane protein 1, mitochondrial precursor Pyridoxal (pyridoxine, vitamin B6) Signal transduction phosphatase
6. Immune Response IPI00001382.2 Transporter 2, ATP-binding cassette, Immune response subfamily B isoform 2 IPI00061977.1 Immunoglobin heavy constant alpha 1 Immune response IPI00382937.7 Immunoglobulin heavy constant mu Immune response IPI00026175.1 Immunoglobulin kappa variable 1–5 Antigen presentation, endogenous antigen IPI00217775.1 Isoform short of HLA class II Cellular defense response/Protein histocompatibility antigen gamma complex assembly chain 7. Regulation of Physical Process IPI00045464.4 Isoform 1 of L-amino-acid oxidase precursor IPI00217030.7 Ribosomal protein S4, X-linked IPI00297779.6 8. Other IPI00219301.5 IPI00030960.2 IPI00000877.1 IPI00032139.1 IPI00011937.1 9. Unknown IPI00005202.1 IPI00003971.1 IPI00016077.1 IPI00295098.3 IPI00555902.1 IPI00333233.1 IPI00550315.1
T-complex protein 1 subunit beta
Integral to plasma membrane Integral to membrane Mitochondrial membrane
Cytoplasm
Endoplasmic reticulum membrane Extracellular region Membrane Extracellular region -
Negative regulation of cell Lysosome proliferation Regulation of progression through cell Ribosome cycle Regulation of progression through cell Cytoplasm cycle
Myristoylated alanine-rich protein kinase C substrate Cytoplasmic FMR1 interacting protein 1 Hypoxia up-regulated 1 Serpin B9 Peroxiredoxin-4
Cell motility
Actin cytoskeleton
lamellipodium biogenesis
-
Progesterone receptor membrane component 2 Isoform RTN1-A of reticulon-1
-
-
-
Glioblastoma amplified sequence Signal recognition particle receptor subunit beta OCIA domain containing 2 isoform 1 7 kDa protein Ig kappa chain C region
-
Integral to endoplasmic reticulum membrane Endoplasmic reticulum
-
Mitochondrial inner membrane -
Protein folding/Response to stress Endoplasmic reticulum Antiapoptosis Cytoplasm Response to oxidative stress/ I-kappaB Cytoplasm phosphorylation
from isolated ECs were separated by one-dimensional gel electrophoresis and visualized by Coomassie staining in preparation for GeLC-MS/MS analysis. The gel lanes were cut into 10–15 consecutive slices of equal size and subjected to tryptic in-gel digestion and tandem MS to analyze all of the detectable proteins in ECs. For each sample, MS and MS/MS spectra were
obtained from each 10–15 nanoLC-MS/MS run. We then applied filtering criterion for the identified peptides based on SEQUEST criteria (XCorr, Rsp, delta Cn, and Probability), as described in Experimental Section, to filter out false-positive identification as shown in Figure 2A. About 600-1000 proteins were identified for each sample (an average of 790 proteins). Journal of Proteome Research • Vol. 7, No. 3, 2008 1143
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Figure 3. Classification comparing the cellular localization (A) and biological process (B) for proteins identified in normal- and cancer tissue-derived ECs. The 48 normal-specific proteins and 85 cancer-specific proteins were classified into categories to characterize proteins specifically expressed in normal- and cancer-derived ECs.
In the filtering analysis, we detected 30% of proteins identified from a single-hit peptide. Proteins with varying numbers of peptides identified by the 1D GeLC-MS/MS strategy were distributed in a pattern that roughly followed single exponential decay (data not shown). To identify specific proteins expressed in normal lungderived or lung cancer-derived ECs, we performed individual comparisons in five cases of paired individual data extracted from ECs in normal versus cancer tissues using ProtAn, an inhouse analytic program for subtraction (Figure 2B). Cancerderived EC proteins showed an average of approximately 60% homogeneity to normal tissue-derived EC proteins. Combined analysis was then performed with individually extracted normal or cancer-specific proteins to find commonly expressed pro1144
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teins in other cancer data sets. We failed to find proteins common to all five cases; however, proteins were detected in common for at least 3 of 5 case data sets. Eighty-five proteins and 48 proteins were collected by combined analysis from cancer and normal data sets, respectively (Figure 2C). A list of identified cancer-specific proteins is shown in Table 2, along with annotations regarding their respective subcellular localization and protein function. We categorized 48 normal-specific proteins and 85 cancer-specific proteins according to cellular localization (Figure 3A) and biological process (Figure 3B) to identify differently expressed proteins. When normal-specific and cancer-specific proteins were compared by biosynthetic process categorization, biosynthesis-associated proteins were detected 3.9-fold more often in cancer-specific EC proteins.
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Lung Cancer-Derived Endothelial Cell Proteome Table 3. Selected Candidate Proteins (16) of Lung Cancer-Derived ECs IPI accession number
protein name
molecular weight (kDa)
average score
case
score
coverage
no. of peptides
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
20.16 30.16 80.23 20.19 30.17 50.19 20.18 10.16 70.24 10.14 30.14 40.16 10.14 30.17 10.14 40.15 20.15 20.18 30.15 30.21 10.15 10.17 20.23 40.21 10.20 20.19 20.19 20.23 10.18 30.20 20.20 20.15 10.16 30.19 10.13 20.16 20.17 10.18 10.21 20.19 20.22 10.22 10.16 20.18 -
2.21 3.44 6.62 2.23 3.51 5.42 3.52 1.70 10.34 1.70 6.40 10.20 2.00 10.87 2.36 9.93 5.20 5.51 5.51 14.76 3.08 2.02 3.66 7.57 2.02 10.93 13.36 13.36 1.75 5.36 5.36 6.87 4.18 14.83 4.06 8.86 11.44 6.70 6.70 14.20 14.20 8.62 8.62 25.00 -
2 4 12 5 5 8 2 1 10 1 4 8 2 4 1 5 4 4 4 3 1 1 3 4 1 3 4 5 1 5 2 3 2 4 1 3 2 1 1 2 4 2 2 2 -
IPI00295857.6
Coatomer subunit alpha
138.2
43.52
IPI00030960.2
Cytoplasmic FMR1 interacting protein 1
145.4
33.51
IPI00001890.6
Coatomer protein complex subunit gamma
97.6
27.68
IPI00061977.1
Immunoreactive heavy constant alpha 1
54
26.81
IPI00032297.2
Isovaleryl coenzyme A dehydrogenase
46
25.15
IPI00030131.2
Thymopoietin, isoforms beta/gamma
50.6
22.67
IPI00017451.1
Splicing factor 3 subunit 1
88.8
20.21
IPI00005202.1
Membrane associated progesteron receptor component 2
23.8
20.20
IPI00024664.1
Splicing isoform long of ubiquitin carboxylterminal hydrolase 5
95.7
20.19
IPI00217030.7
40S ribosomal protein S4, X isoform
29.4
20.17
IPI00011937.1
Peroxiredoxin 4
30.5
16.82
IPI00178440.2
Elongation factor 1-beta
24.6
15.20
IPI00412545.4
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 5
13.5
13.52
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Table 3. Continued IPI accession number
protein name
molecular weight (kDa)
average score
case
score
coverage
no. of peptides
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
10.17 18.21 10.23 10.15 10.14 10.14 10.16 10.12 10.14 -
6.28 13.46 6.28 1.53 1.53 1.53 9.09 9.09 10.39 -
2 3 2 5 3 2 2 1 2 -
IPI00216587.6
Ribosomal protein S8
24
12.87
IPI00001382.2
Transporter 2, ATPbinding cassette, subfamily B isoform 2
71.9
10.14
IPI00555902.1
Ovarian carcinoma immunoreactive antigen-like protein
16.9
10.14
Among those, proteins related to protein biosynthesis were especially predominant. Proteome Data Analysis To Select Possible Candidate Proteins. To find reliable candidate proteins for NSCLC in lung cancer-derived EC data sets, we selected from the 85 cancerspecific proteins 44 proteins that had not previously been detected in other normal-derived EC data sets. We then assessed identification patterns of these proteins in all five cases. Of these 44 proteins, 9 proteins were found in 4 cases including proteins identified by a single-hit peptide. To further select proteins most reliably specific for lung cancer-derived EC proteins, we considered the number of identified peptides for each protein and examined whether the proteins were present in the correct molecular mass region of the gel. With these criteria, we narrowed the list to 16 potential candidate proteins that were reliably identified only in cancer-derived ECs (Table 3). Validation of Candidate Proteins. To validate the cancerselective character of candidate proteins identified by MS/MS, we performed Western blot analysis for those proteins that were not yet described in lung cancers, especially for ECs. COPG was identified as cancer-specific in 4 of 5 cases with an average 27.7 SEQUEST score. COPG was specifically detected in cancerderived ECs in 3 of 5 experimental case sample sets and in an additional 3 cases, which was consistent with MS/MS data (Figure 4A). PRDX4 was identified in 3 of 5 cases of cancerderived EC data sets, with a low, average 16.8 SEQUEST score. Although PRDX4 was expressed to some degree in normalderived ECs, the data showed that PRDX4 had generally higher expression in cancer-derived ECs than in normal-derived ECs (Figure 4B). To confirm whether candidate proteins were overexpressed in ECs within cancer tissue, we performed immunohistochemistry. COPG staining was greater in the microvascular endothelium in 7 of 16 (44%) lung cancers, and in the cytoplasm in 10 of 16 (63%) cancer cells, compared to staining in pairednormal counterparts taken from the same patients (Figures 5B,C and 6A,B). The corresponding COPG staining in pairednormal lung tissues was minimal or absent as shown in Figure 5A. PRDX4 staining was robust in microvascular ECs in 13 of 16 (81%) lung cancers, and in the cytoplasm of 11 of 16 (69%) cancer cells, compared to paired-normal counterparts (Figures 5E,F, and 6C,D). In contrast, paired adjacent-normal lung tissue cells rarely showed PRDX4 staining, and microvasculature (Figure 5D). TMPO staining showed expression in microvas1146
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culature in 9 of 16 (56%) cancers and demonstrated strong nuclear labeling in 10 of 16 (63%) cancers (Figures 5H,I and 6E,F). TMPO labeling in normal lung endothelium and epithelium was minimal (Figure 5G).
Discussion Proteomic approaches can be applied to study the expression of specific proteins or to profile tumors by surveying the expression of the entire proteome. Recent advancements in proteomic techniques, such as MudPIT or GeLC-MS/MS have overcome previous disadvantages of common techniques such as 2D-gel electrophoresis MALDI-TOF-MS analysis.18–20 Shotgun proteomic techniques make it possible for comprehensive analysis of protein expression patterns in tissues, sera, and other human materials, therefore, making it possible to understand the molecular complexities of human tumors.21 In the present study, we performed comparative molecular mapping of normal- and cancer-derived EC proteome to demonstrate heterogeneity of ECs affected by microenvironment and presented 16 candidates for lung cancer EC-specific proteins, which may be important in the pathogenesis and progression of NSCLC. Because of minor tissue compartments of endothelium, one of the major limitations in the survey of EC heterogeneity was the difficulty of isolating EC cells from organs.22 Here, we isolated ECs within human NSCLC and adjacent-normal tissues from five patients with lung cancer by means of an immunomagnetic isolation method using CD31 antibody, which is known as an endothelial cell specific marker. CD31, also known as PECAM-1 (platelet endothelial cell adhesion molecule-1), is expressed exclusively in endothelial cells and platelets, with the exception of certain cell subsets of myeloid lineage; however, the expression of CD31 on myeloid cells or platelets does not interfere with our isolation procedure because these cells are labile.23 Because CD31 is constitutively expressed in ECs,24 it is widely used as an endothelial marker to enrich ECs in various tissues or tumor/normal tissues.25–27 Further we found 16 EC associated marker proteins in our human lung endothelial proteome data sets, compared with 73 known vascular EC surface proteins reported by Durr et al.12 EC protein profiles were produced using GeLC-MS/MS, resulting in an average of 800 proteins from five pairs of normal- and cancer-derived lung ECs, which is a greater number of proteins than that identified by 2-D gel analysis.19,20
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Figure 4. Validation of candidate protein expression by Western blot analysis. Proteins from lung cancer and matching adjacent normalderived ECs of 7 patients (experimental sample sets plus the samples from another extra cases) were loaded onto SDS-PAGE gels, and the blotted membranes were probed with COPG antibody (A) and PRDX4 antibody (B). COPG, coatomer protein complex, subunit gamma; PRDX4, peroxiredoxin 4; Normal, normal lung-derived ECs; Cancer, lung cancer-derived ECs.
Figure 5. Immunohistochemial staining of COPG, PRDX4, and TMPO in NSCLC tissues and normal lung tissues from human patients. In NSCLC tissues, COPG (B and C), PRDX4 (E and F), and TMPO (H and I) staining was mainly associated with the microvasculature (arrowheads) and cytoplasm or nucleus of cancer cells in cancer specimens. In contrast, in control normal lung tissues (A, D, and G), these proteins were rarely seen in the microvasculature (arrows) or normal epithelium. In B, E, and H, lower magnification view of the respective tissue type is shown (×200). Boxed areas are displayed at higher magnification in C, F, and I (×400). Bars, 100 µm.
Because this method is largely unbiased, we identified lowabundance proteins, high-molecular-weight proteins, and proteins with extreme pIs, which could not be detected on 2Dgels. In an effort to discover lung cancer-derived EC specific biomarkers, we performed individual data comparison to identify molecules particularly modulated within each patient. Cancer-derived EC proteins extracted from subtractive individual comparison showed on average difference of about 40% of normal, implying that the EC proteome is adjusted differently based on a normal or diseased microenvironment. We then collected 16 cancer-specific proteins which were detected in
at least 3 of 5 cancer data sets, and were not found in any paired-normal data sets. One of the important considerations in performing subtractive proteome analysis between two groups is that the ‘difference’ proteome list acquired from the MS/MS analysis is generated mainly by two criteria. First, the molecule is biologically absent in control cells, but present in cancer cells, and the protein is sufficiently abundant to be detected by the mass spectrometer. Second, the protein is present in both control and cancer cells, but the protein level in the control is lower than the detection sensitivity of the mass spectrometer, while the protein level in the cancer cells is high enough to be detected. In other words, the sensitivity of the Journal of Proteome Research • Vol. 7, No. 3, 2008 1147
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Figure 6. Evaluations of grade are scored by the intensities of positive localizations in endothelium (left panels) and epithelium (right panels) in lung cancer and paired-normal lung tissues from 16 lung cancer patients after immunohistochemistry. Gray-circle and blackrhombic shapes indicate the grade of normal and cancer specimens, respectively. (A and B) COPG-positive localizations show higher intensities in cancer specimens. (C and D) PRDX4-positive localizations are stronger in the endothelium of cancer specimens compared to those in normal specimens. (E and F) TMPO-positive localizations are observed more in cancer specimens than in normal specimens. Grade: 0, - (negative); I, + (weak); II, ++ (moderate); III, +++ (strong); IV, ++++ (strongest) cytoplasmic staining.
mass spectrometer creates a threshold of detection which distinguishes the two cases. This is one explanation for the observation that some candidate proteins are detected in both normal- and cancer-derived EC samples when subjected to Western blot analysis, which is more sensitive method of protein detection. With our approach, we discovered 16 potential candidate proteins for NSCLC-derived EC-enriched protein biomarkers (Table 3). This list contains several proteins known to be highly expressed in cancer cells compared to normal cells. Among these proteins, NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 5 (NDUFA5), peroxiredoxin 4 (PRDX4), and thymopoietin (TMPO) were previously reported as potential cancer-related molecules. NDUFA5 is the B13 subunit of NADH dehydrogenase (also called NADH quinine oxidoreductase) of the respiratory chain. NADH quinine oxidoreductase gene expression is induced in response to a variety of agents, including oxidants, antioxidants, and ionizing radiation,28 and is known to be altered in many cancer tissues, including breast, colon, and lung cancers.29–31 Shimada et al. showed evidence that upregulated NDUFA5 expression may play a role in cervical carcinogenesis through acquiring growth advantage and resistance against an apoptotic signal. Further studies are necessary to elucidate the dysregulated cell signaling and proliferation induced by NDUFA5 overexpression.32 PRDX4 is a member of the peroxiredoxin family which exhibits several regulatory and antioxidative characteristics, and 1148
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may represent an important protein family in terms of both tumor invasion and resistance.33 Several proteins of the peroxiredoxin family are highly overexpressed in certain carcinomas.34–36 Lehtonen et al. showed patterns of highly overexpressed peroxiredoxins in various lung cancer subtypes. PRDX4 was elevated most markedly in adenocarcinoma.37 Moreover, PRDX4 may be important in tumors, since it is located both intra- and extracellularly.38 In our study, PRDX4 was identified in 3 of 5 cases by GeLC-MS/MS analysis (Table 3), and was also shown to be highly expressed in cancer-ECs compared to normal-ECs by Western blot analysis (Figure 4B). Additionally, immunohistochemical analysis demonstrated strongly elevated PRDX4 expression of in 81% microvascular endothelium in lung cancer compared to paired adjacent-normal lung tissues (Figures 5D,F, and 6C,D). Lungs are continuously exposed to higher levels of oxygen and oxidants compared to other cell types, and endothelium, which represents the interface of blood and tissue, is an area which directly undergoes oxidative stress. This finding suggests that PRDX4 expressed in endothelium may play a role as a barrier against reactive oxygen species in both carcinogenesis and tumor progression. TMPO isoforms β/γ was identified in 4 of 5 cases of cancerderived EC data sets, with an average 22.7 SEQUEST score. TMPO nuclear labeling by immunohistochemical analysis was observed in vascular endothelium in 56% of cancer samples (Figure 5G-I). TMPO is also known as lamin-associated polypeptide 2 (LAP2), and LAP2 proteins are expressed as a
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Lung Cancer-Derived Endothelial Cell Proteome variety of isoforms (R, β, γ, δ, , and ζ) by alternative splicing events in a single gene.39,40 LAP2 is a component of nuclear lamina, located between the inner nuclear membrane and peripheral chromatin.41,42 Changes in the composition of the nuclear lamina are observed in most hematological malignancies, suggesting that this protein is involved in tumorigenesis.43 Thus, it is not surprising that TMPO was upregulated in the cancer-associated ECs and also in the lung cancer cells in our study. COPG is a subunit of coatomer protein (COP)I, a heptomeric protein complex.44 COPI exists as a soluble, cytosolic complex that is recruited to the Golgi membrane during the formation of a COPI-coated transport vesicle. COPI is one type of proteincoated vesicles involved in trafficking pathways between the endoplasmic reticulum and Golgi apparatus in the eukaryotic cells. By quantitative proteomics analysis, Gilchrist et al. reported a role for COPI vesicles in recycling and cisternal maturation.45 Though COPG is understood to be a component of COPI, the role of COPG in cancer-derived ECs is not clear, and little is known about the expression of COPG in carcinogenesis. In recent studies, Wu et al. reported that the Cdc42 binding to the COPG subunit is necessary for Cdc42-induced malignant cellular transformation. Identification of COPG as a target for Cdc42 suggests that it may act to regulate cell growth. In addition, this finding highlights a previously unappreciated connection between the action of Cdc42 in the Golgi, and provides a potentially important clue to the specificity underlying Cdc42 transforming activity.46 Thus, it is also an interesting finding that COPG is highly and specifically expressed in cancer-derived ECs. This suggests that ECs in cancer tissue may undergo early transformation accelerated by COPG. In our analysis, COPG was detected in 4 of 5 cases (80%) of cancerderived ECs by MS/MS analysis, and when analyzed by Western blot analysis, these samples showed robust expression of COPG even in additional samples (Figure 4A). COPG labeling was observed by immunohistochemical analysis in lung cancerderived ECs (44%) and in lung cancer cells (63%) (Figure 5A-C). These data suggest that elevated COPG protein levels in lung cancer cells may influence neighboring ECs to express higher levels of COPG than in normal-derived ECs. Further studies about the role of COPG in cancer-derived ECs may reveal the importance and mechanism of the transformation of ECs which are in contact with lung cancer cells.
Conclusions We have described a large-scale proteomic analysis of normal- and cancer-derived lung EC proteins. Our findings suggest that molecular heterogeneity exists in endothelial cells derived from normal and cancer tissues. Also, we have identified lung cancer-derived EC-enriched proteins that may be available for use as NSCLC diagnostic biomarkers or therapeutic targets in the future.
Acknowledgment. This work was supported by a grant (RTI04-01-01) from the Regional Technology Innovation Program of the Ministry of Commerce, Industry and Energy (MOCIE), and by a grant (FPR06A1-521) from the Functional Proteomics Research Center of the 21st Century Frontier Research Program funded by the Korean Ministry of Science and Technology, Republic of Korea.
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PR7007237
