Comparative Plasma Proteome Analysis of Lymphoma-Bearing SJL

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Comparative Plasma Proteome Analysis of Lymphoma-Bearing SJL Mice Vadiraja B. Bhat,† Man Ho Choi,† John S. Wishnok,† and Steven R. Tannenbaum*,†,‡ Biological Engineering Division and Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 56-731A, Cambridge, Massachusetts 02139 Received May 18, 2005

In SJL mice, growth of RcsX lymphoma cells induces an inflammatory response by stimulatingVβ16+ T cells. During inflammation, various serum protein levels can increase (e.g., acute phase reactants) or decrease (e.g., albumin), and most of these altered proteins are thus potential biomarkers. Although blood plasma is a valuable and promising sample for biomarker discovery for diseases or for novel drug targets, its proteome is complex. To address this, we have focused on a comprehensive comparison of the plasma proteomes from normal and RcsX-tumor-bearing SJL mice using the 1D-Gel-LCMS/MS method after removing albumin and immunoglobulins. This analysis resulted in the identification of a total of 1079 nonredundant mouse plasma proteins; more than 480 in normal and 790 in RcsX-tumor-bearing SJL mouse plasma. Of these, only 191 proteins were found in common. The molecular weights ranged from 2 to 876 kDa, covering the pI values between 4.22 and 12.09, and included proteins with predicted transmembrane domains. By comparing the plasma proteomic profile of normal and RcsX-tumor-bearing SJL mice, we found significant changes in the levels of many proteins in RcsX-tumor-bearing mouse plasma. Most of the up-regulated proteins were identified as acutephase proteins (APPs). Also, several unique proteins i.e., haptoglobin, proteosome subunits, fetuin-B, 14-3-3 zeta, MAGE-B4 antigen, etc, were found only in the tumor-bearing mouse plasma; either secreted, shed by membrane vesicles, or externalized due to cell death. These results affirm the effectiveness of this approach for protein identification from small samples, and for comparative proteomics in potential animal models of human disorders. Keywords: lymphoma • tumor • inflammation • mouse • comparative • plasma biomarker • proteomics • mass spectrometry • spectrum mill

Introduction An ideal animal model for a disease is one in which all animals of a strain develop the disease spontaneously. In this context, SJL miceswhich were derived from the Swiss-Webster strain1sare a good choice as a model for autoimmune diseases since they exhibit multiple immunological disorders, e.g., paraproteinemia2 and myopathy.3,4 This condition is characterized by up-regulations of iNOS, and the myopathy can be reversed by treatment with an iNOS inhibitor.5 These mice are suspected of having defective suppressor T-cell function6 and, during the first year of life, spontaneously develop B cell lymphomas (RCS), which are produced in germinal lymphoid centers.1,7 The RCS tumor cells express a unique superantigen (vSAG), encoded by an endogenous MMTV provirus that stimulates Vβ16+ Th cells8 to secrete a cytokine (γ-interferon),9 which is required for growth of the lymphoma cells in vivo and in vitro.10,11 Intraperitoneal injection of RcsX cells into SJL mice leads to rapid tumor growth as well as infiltration of host T * To whom correspondence should be addressed: Tel: (617) 253-3729. Fax: (617) 252-1787. E-mail: [email protected]. † Biological Engineering Division. ‡ Department of Chemistry.

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cells into the lymph nodes, spleen, and liver, resulting in morbidity after about 15 days.12 In the course of immune response, iNOS expression was induced in macrophages located in the spleen and lymph nodes, resulting in a 50-fold increase in nitric oxide (NO) production within 14 days of RcsX cell injection.13 NO, a free radical present in inflammatory conditions associated with malignancies,14,15 induces cellular damage and mutations.15-17 SJL transgenic mice were initially developed to study in vivo toxicological responses to excess NO• production in healthy animals.5,12 Under pathophysiological conditions, excess NO and/or other reactive species generated during chronic inflammation by macrophages induce cellular damage in close proximity to the activated macrophages13,15 and release cellular proteins into the blood circulating system. Also, during inflammation, levels of several serum/plasma proteins will change (up regulate or down regulate), including acute phase proteins (APPs).18 Blood proteins are useful targets for diagnostic, prognostic, and/or therapeutic development. With recently available proteomic tools, profiling of the human plasma proteome has become more feasible in searching for disease-related markers.19 Although animal models such as rat and mouse are extensively 10.1021/pr0501463 CCC: $30.25

 2005 American Chemical Society

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Proteomic Analysis of Lymphoma Mouse Plasma

Figure 1. Flowchart illustration of the Gel-LC-MS/MS strategy used for comparative mouse plasma proteome experiments.

used to study human diseases and for drug discovery, relatively little progress has been made toward the proteomic characterization of animal body fluids. Currently, most of the mass spectrometry (MS) based proteomics techniques use a variety of pre-fractionation and separation steps, prior to analysis, at the protein level as well as at the peptide level.20-24 The complexity of the plasma proteome is due to several factors, including the wide dynamic concentration range of the proteins (,pg to .mg/mL), a variety of post-translational modification, and large numbers of splice variants of immunoglobulins (Igs).25 Several multidimensional methods have been developed to dissect the human serum/plasma proteome, with or without depletion of major proteins (albumin, IgG, etc), to increase the number of proteins detected. In this study, we compared the plasma proteome of normal and RcsX-tumor-bearing SJL mice, using a 1D-Gel-LC-MS/MSbased approach (shown schematically in Figure 1), as an alternative approach for 2DE or MudPIT. It combines immunoaffinity depletion of major plasma proteins, SDS-PAGE and mass spectrometric analysis of in-gel digested peptides as well as Spectrum Mill (Agilent) for nonredundant protein identification and semiquantitative estimates of their relative abundance. A total of 480 in normal and 790 in RcsX-tumor-bearing mouse nonredundant plasma/serum proteins were identified and bioinformatically annotated according to their physicochemical characteristics such as molecular weight, pI, and TM domain, subcellular location annotated in Swiss-Prot database

and Amigo database or predicted by PSORT and function family categorized from universal Gene Ontology (GO) annotation terms. Additionally, we confirmed some of the potential biomarkers in multiple samples either by Western Blot or mass spectrometry. This strategy has proved to be a relatively highthroughput, sensitive, effective, and largely unbiased analytical approach for comparative plasma/serum proteomics.

Materials and Methods Reagents and Chemicals. Ultrafree-MC HV centrifugal filter units (0.45 µm) were purchased from Millipore (Billerica, Mass., USA). Protein A-Agarose and Protein G-Agarose beads were purchased from Santa Cruz Biotechnology, Inc. (Santa Cruz, California, USA). Rabbit polyclonal anti-mouse albumin antibodies were purchased from Research Diagnostics, Inc. (Flanders NJ). Tris-(2-carboxyethyl) phosphine hydrochloride (TCEP), from Pierce (Rockford, IL). Microcon separation tubes and ZipTip C18 pipet tips were from Millipore (Bedford, MA). Trypsin was purchased from Promega (Madison, WI). Trisglycine gels were from Bio-Rad Laboratories (Hercules, CA). SimplyBlue Safestain was from Invitrogen Life Technologies (Carlsbad, CA). Trifluoroacetic acid (TFA) and dithiothreitol (DTT), were from Sigma (St. Louis, MO). Acetonitrile was from Merck (Whitehouse Station, NJ.). Samples. Male SJL mice (n, 6), 8 weeks old (The Jackson Laboratory), were fed a low nitrate control diet (AYN-76A, BioServe, Frenchtown, NJ), then weighed and placed individually in cages. Two days later, mice were injected intraperitoneally Journal of Proteome Research • Vol. 4, No. 5, 2005 1815

research articles with 0.2 mL of PBS containing 107 cells of the RcsX line (provided by N. Ponzio, University of New Jersey Medical Center, Newark, NJ), isolated from lymph nodes of mice bearing the actively growing tumor. The animals were maintained under normal feeding conditions. The animals were anaesthetized with isoflurane and blood samples were collected in the morning 14 days after injection of cells, by cardiac puncture in EDTA containing tubes and the plasma was stored at -80 °C for later analysis. In the present study, we used plasma samples from one normal and one RcsX-tumor mouse. For validation of our findings (haptoglobin and MAGE-B4), we used plasma samples from three normal and three tumor-bearing mice for immuno blot analysis. Removal of Albumin and IgG from Mouse Plasma Samples. A 200-µL portion of Protein A-Agarose was added to centrifugal filter units and washed twice with PBS buffer. Anti-mouse albumin antibody was added to the spin filter units and incubated with protein A- Agarose at room temperature for 1-2 h with rotation, then washed several times for removal of unbound proteins, by spinning for few seconds in a benchtop minicentrifuge, without drying the affinity beads. For the removal of both albumin and IgG in a single column, 0.15 bed volume of protein G Agarose beads were added to the immunoaffinity resin. In these pre-packaged affinity spin filter columns, 1:200 diluted (in PBS) mouse plasma (2 µL) was incubated for 1-2 h at room temperature with constant rotation, then spun down (20 s) and the flowthrough was collected. The column was then washed three times, first with 100 µL of PBS, followed by two washes with 400 µL of PBS, and all washes were combined as the unbound fraction of proteins. The unbound fraction of proteins was desalted by using 3000 Da cutoff microcon centrifugal filter units and concentrated to dryness in a SpeedVac. Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) and In-Gel Digestion. Sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE) was performed in a Mini Protean III-cell (Bio-Rad) using Tris-glycine, 8-16% gradient gel with 0.1% SDS, according to the instructions of the manufacturer. Prior to analysis, dried protein samples were resuspended in 25 µL SDS-PAGE buffer (2% mercaptoethanol (v/v), 1% SDS, 12% glycerol, 50 mM Tris-HCl and a trace amount of bromophenol blue), heated at 95 °C for 5 min, cooled and loaded directly into the gel. Gels were analyzed after being stained with SimplyBlue Safestain (Coomassie-based staining) reagent. The entire gel lane was then cut into 30 equal sized gel slices, proteins were digested in-gel, and peptides were extracted as described earlier by Shevchenko et al.,26 with a few minor modifications.27 The extracted peptides were concentrated in a SpeedVac to ∼10 µL, and cleaned and desalted with C18 zip-tips. The desalted peptides were dried completely in a SpeedVac and redissolved in 0.6 µL of 0.1% TFA for LC-MS/MS analysis. Mass Spectrometric Analysis of Tryptic Peptides. Fusedsilica capillary columns (75 µm i.d. × 360 µm o.d.; 14 cm length, tip 8 µm, New Objective, Worburn, MA) were packed in-house, with 5-µm C18 reverse-phase material (Protein&peptide C18; Vydac, Hesperia, CA).28,29 The peptides were injected onto the columns with a Rheodyne injector (0.5 µL internal loop) and separated with a 90-minute linear gradient of 0 to 80% buffer B (0.08% TFA in 95% acetonitrile containing 5% methanol), at a flow rate of 120 nL/min, and then ramped back to the initial conditions [100% buffer A (0.1% TFA in 5% acetonitrile)] over an additional 60 min. Full-scan MS spectra were acquired over 1816

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the m/z range of 400-2000, using an Applied Biosystems (Framingham, MA) QSTAR XL quadrupole-time-of-flight (TOF) mass spectrometer equipped with a nanospray source (Proxeon Biosystems, Odense, Denmark). The electrospray interface design uses a micro-tee (Upchurch Scientific, Oak Harbor, WA) with a 1-in. piece of platinum rod, inserted into one arm of the micro-tee, to supply the electrical connection. The electrospray voltage was typically 2.3-2.7 kV applied just upstream of the column. Data-dependent MS/MS analysis was performed on the three most intense peaks in each full-scan spectrum, using double and triple charge-states (most of the non-peptide background constituents are singly charged). Accumulation time and pulsar frequency were maintained at 3 s and 6.99, respectively; the mass tolerance was 50 mmu and the collision gas pressure setting was 6. Data Processing and Analysis. Tandem MS spectra were searched against the National Center for Biological Information nonredundant (NCBInr) mouse protein database, using Spectrum Mill.30 This software includes a Data Extractor function that identifies good quality MS/MS spectrum for peptides by seeking CID fragment differences that correspond to known amino acids (sequence tag length >1) and thus functions as a filter to discard spectra that are unlikely to arise from peptides. By doing this, Spectrum Mill reduces the number of MS/MS spectra by about 35-40% and minimizes false positive identifications prior to searching the protein databases. To minimize false-positives, the extracted MS/MS spectra were searched against the NCBInr mouse proteome database for tryptic peptides only, with one allowed missed cleavage, in ‘identity mode’ to find unmodified peptides. This step was repeated in ‘homology-multi mode’ to search for mutations, post-translational modifications and chemical modifications. Search parameters were as follows: MS and MS/MS tolerance of 100 and 500 ppm, tryptic specificity allowing for up to one missed cleavage and fixed modification of carbamidomethylation of cysteine in identity mode and variable modification of oxidation of methionine (m), pyro-glutamic acid (q) and phosporylation of serine (s), threonine (t) and tyrosine (y) in homology-multi mode (mqsty). Spectrum Mill incorporates an algorithm that generates numerical scores for quality of identification for both peptides and proteins. The default thresholds considered to represent high-quality results are as follows: a.) in protein details mode; protein score >20, peptide score (scored percent intensity [SPI]) charge +1 (>9, >70%), peptide charge +2 (>9, >70%), peptide charge +3 (>9, >70%), peptide charge +4 (>9, >70%); b.) in peptide mode: SPI charge +1 (>13, >70%), peptide charge +2 (>13, >70%), peptide charge +3 (>13, >70%), peptide charge +4 (>13, >70%). All autovalidations for our data were based on these thresholds. Additionally, lower spectra scores (>9, >65%) were evaluated by visual examination and only good quality spectra were validated (acceptable signal-to-noise and the presence of at least three consecutive b or y ion fragments). The above parameters result in a protein being considered identified when either multiple spectra of moderate-to-good quality and at least 1 spectrum of excellentto-good quality are obtained. Only proteins identified according to the above criteria were considered in developing the conclusions with respect to potential biomarkers. The complete list of proteins identified by a single unique peptide in normal and tumor-bearing mouse plasma samples is available on request. In cases of multiple protein database entries, based on the same set of peptides, only a single entry (highest molecular weight) was considered. A rough estimate of changes

Proteomic Analysis of Lymphoma Mouse Plasma

in the levels of proteins, based on number of MS/MS spectra, for a particular protein in RcsX-tumor-bearing SJL mouse plasma alongside its normal counterpart was performed using Spectrum Mill’s semiquantitative comparison capability. Details of comparison between normal and tumor-bearing-mouse samples are discussed in the results and discussion section. Bioinformatics Annotation Tools. The theoretical pI value calculation is an analysis routine within Spectrum Mill. The protein subcellular location and functional class were categorized according to GO annotation terms31 extracted by InterPro (http://www.ebi.ac.uk/interpro/),32 from the Swiss-Prot and TrEMBL protein databases (us.expasy.org/sprot/). The PSORT (http://psort.nibb.ac.jp)31,33 tool was used to predict protein subcellular location for proteins with no GO information in Swiss-Prot and TrEMBL databases. Prediction of transmembrane domains (TMH) was done with the TMHMM 2.0 algorithm (www.cbs.dtu.dk/services/TMHMM/).34 Western Blot Analysis. Plasma samples were separated by 10% SDS-PAGE and then transferred onto PVDF membranes for 2h at 40V (Bio-Rad). After blocking the membrane with 5% milk solution in PBS at room temperature, the membrane was incubated with rabbit polyclonal anti-rat haptoglobin antibody or rabbit polyclonal anti-human MAGE-B4 antibody. The membrane was incubated with peroxidase-conjugated secondary antibody after washing in PBS and detected using chemiluminescence with an ECL kit (Amersham Biosciences).

Results and Discussion Mouse Plasma Proteome Analysis by 1D-Gel-LC-MS/MS after Immuno-Affinity Removal of Albumin and Immunoglobulin. Sample Processing. Body fluids, e.g., blood plasma, serum, CSF, contain thousands of proteins over a wide dynamic concentration range. Low abundance plasma proteins, which are potential biomarkers, constitute less than 1% of the plasma proteome, while only 24 high-abundance proteins constitute the remaining 99%. Albumin, which accounts for over 50% of the total plasma protein,25,35 presents a challenge for plasma proteome analysis since it is a major transport protein and its removal may also result in the removal of associated proteins and peptides. The sequence variability of immunoglobulins further complicates these analyses. While it is difficult to remove all of the high-abundance proteins, removing the two major contributorssalbumin and immunoglobulinscan significantly reduce the overall complexity and thus aid in the detection of low-abundance proteins. Several affinity methods, including immunoaffinity, have been developed for removal of human serum albumin prior to MS analysis.21,22,36 Most of the commercially available depletion kits are specific to human samples (while this manuscript was in preparation, Agilent announced a protein depletion kit for mouse and rat). In the present study, both normal and RcsXtumor-bearing mouse plasma samples were analyzed by 1-DSDS-polyacrylamide electrophoresis before and after the depletion of major proteins (Figure 2). Although SDS-PAGE indicated that the immunoaffinity spin-column depleted essentially all of the immunoglobulins and albumin, it is impossible to completely remove albumin, or any other protein, from a given sample. We obtained a 20% sequence coverage for mouse serum albumin with 9 unique peptides (9 in normal mouse and 5 in tumor mouse) even after albumin depletion. In fact, serum albumin is a negative APP in mouse, during acute inflammation; its level tremendously decreases.18 When SDS-

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Figure 2. SDS-PAGE analysis of albumin- and immunoglobulindepleted normal and RcsX-tumor-bearing SJL mouse plasma (Simply Blue Safe Stain). SDS-PAGE (mini gel) was performed in a Mini Protean III-cell (Bio-Rad) using Tris-glycine, 10% gradient gel with 0.1% SDS. lane 1, molecular weight markers; lane 2 and 4, undepleted and depleted normal mouse plasma (2 µL); lane 6 and 8, undepleted and depleted RcsX-tumor-bearing mouse plasma (2 µL).

PAGE was visualized with Simply Blue Safe Stain, the intensities of some of the protein bands were increased after depletion of albumin and immunoglobulin (Figure 2). 1D-Gel-LC-MS/MS Analysis of Mouse Plasma. Two-dimensional PAGE (2DE) and MudPIT (shotgun proteomics) methods are commonly used to analyze complex proteomes. Although both 2DE and SCX chromatography can decrease the complexity of the samples, they typically require more than 200 µg of protein as starting material for complete proteome analysis.23,35 Also in 2DE, it is difficult to analyze insoluble membrane proteins or proteins with extreme MW and pI values. Thus we used 1D-SDS-PAGE, rather than 2DE for separation of proteins or SCX chromatography for the separation of peptides, as a sample preparation step prior to MS analysis. By coupling the 1D-SDS-PAGE protein separation to nano-capillary LC-MS/ MS analysis, we were able to analyze and compare the mouse plasma proteome, using 2 µL of plasma sample, i.e., < 100 µg of total protein (due to dilution of blood with anticoagulant while collecting the samples). As reported earlier,35 albumin and IgG together constitute ∼71% of total protein in human serum or plasma. If 99% of the albumin and immunoglobulins can be removed, the total protein after depletion will be ∼30 µg as a starting amount for the mouse proteome analysis. Using albumin and immunoglobulin depletion, 1D-SDSPAGE and nanoflow capillary reverse-phase LC-MS/MS, followed by data analysis with Spectrum Mill, we have identified 1079 mouse NR proteins in normal and in tumor-bearing SJL mouse plasma (480 in normal and 790 in tumor). Spectrum Mill extracted a total of 29 432 raw MS/MS spectra together from normal and tumor-bearing mouse plasma, resulting in the identification of 8640 tryptic mouse peptides with high confidence (Table 1), i.e., 3-fold higher in tumor-bearing mouse plasma. Surprisingly, we did not find fetuin-B in normal mouse plasma, although, Journal of Proteome Research • Vol. 4, No. 5, 2005 1821

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ered as one of the major APPs, acting as a high-affinity hemoglobin-binding protein and an antioxidant.52 One of our more intriguing observations is the apparent absence of haptoglobin in SJL mouse plasma. Haptoglobin (HP) is one of the core sets of APPs in most vertebrate species, and its synthesis is generally increased severalfold during inflammation. In mice, this increase can be exceptionally high, e.g., as much as 30-fold.52 During RcsX tumor induced inflammation in the SJL mouse, we found that the level of haptoglobin increases tremendously and it was identified with 15 unique peptides (Table 2). In a separate analysis of normal human plasma, we identified haptoglobin with 19 unique peptides (41% sequence coverage; data not shown). The absence of haptoglobin in SJL mouse plasma was confirmed by searching all MS/MS data against an independent mouse haptoglobin database created in Spectrum Mill. In a separate experiment, three different SJL mouse and one C57BL/6 mouse plasma samples were separated by 1D-SDS-PAGE and the regions corresponding to haptoglobin (37-40 kDa) were cut, digested in-gel, and analyzed by nano-LC-MS/MS. Only one unique peptide (DITPTLTLYVGK) for haptoglobin was found in C57BL/6 mouse plasma digest, but none were detected in the SJL mouse plasma digest. These results were confirmed by Western blots with rabbit polyclonal anti-rat haptoglobin antibody. Haptoglobin remained undetectable in SJL mouse plasma in comparison to the tumor-bearing SJL mouse plasma, C57BL/6 mouse plasma and normal human plasma, where intense haptoglobin bands were observed (Figure 6A). This may simply reflect very low abundance of haptoglobin in SJL mice, and therefore, it should probably be confirmed via additional experiments, e.g., gene expression data analysis.

Figure 4. Relative numbers of proteins identified in different sub cellular location and functional category. Distribution is shown in both in actual numbers from 1079 of total proteins (x-axis) and their relative numbers in % from normal and tumor mouse plasma (next to each bar). Black bars, normal mouse; light gray bars, RcsX-tumor-bearing mouse. A: Subcellular distribution. B: Functional categories

in the tumor-bearing mouse, its level was increased and it was identified with 3 unique peptides (13 spectra). Recently, Hsu et al., reported that overexpression of fetuin-B in skin squamous carcinoma cells led to suppression of tumor growth in nude mice.45 Clusterin (CLU) is another serum glycoprotein that has been paradoxically observed to have both pro- and antiapoptotic functions with high-density lipoprotein particles.46,47 We have identified CLU in tumor-bearing mouse plasma with 10 unique peptides (17 spectra) and in normal mouse plasma with 2 unique peptides (3 spectra). Wellmann et al., reported that CLU is a tumor-specific marker in lymphoid neoplasms using gene array technology,48 and it is also thought to be cytoprotective role during autoimmune diseases and inflammation.49 Haptoglobin is a major glycoprotein found in most of the reported human serum proteomes.22 Recent reports suggest that it is significantly upregulated in ovarian cancer patients and that it can therefore be used as a novel circulating biomarker for ovarian cancer.50,51 Haptoglobin is also consid1822

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Although APPs are not tumor-specific markers, they have potential value as biomarkers and can be used to evaluate cancer-related inflammation. Apart from APPs, we also found several unique proteins (Table 2) that are potentially involved in the inflammatory response and may serve as biomarkers for cancer-related inflammation. Several proteasome subunits were identified and some of these were found only in tumor-bearing mouse plasma along with a global marker of cytolysis, LDH, with 8 unique peptides. Lavabre-Bertrand et al., reported that the plasma proteasome level is a potential marker for solid tumors and hemopoietic malignancies and it may correlate with LDH levels in plasma.53 Also, several other cytoplasmic and nuclear proteins, including 14-3-3ζ, MAGE-B4, NDP kinase B, histone H4, L-plastin, dystonin isoform e, were found only in tumor-bearing mouse plasma (Table 2). During inflammation and cellular damage, a few cytoplasmic and nuclear proteins will release to circulating system. For example Hsp70 a cytoplasmic protein is rapidly released to the circulating system after inflammation and myocardial damage in myocardial ischaemia.54 L-plastin (LPL), a leukocyte-specific actinbinding protein that has been implicated in regulating PMN signal transduction55 and also as a potential metastatic marker,56 was identified with 3 unique peptides. 14-3-3ζ is a cytoplasmic protein identified in normal human plasma21 and in CSF as a nonspecific marker of neuronal injury57 and may play a role in MAPKAPK2-mediated inflammatory process58 and in neutrophil adhesion.59 14-3-3ζ could be a good cancer biomarker for pancreatic adenocarcinoma60 and was identified with 4 unique peptides only in tumor-bearing mouse plasma (Table 2). One of the interesting proteins identified in the present study was melanoma antigen, family B4 (MAGE-B4) with 2 unique peptides, VTLVDSSCK (Score, 13.43; SPI, 83.8%) EANSDPPS-

Proteomic Analysis of Lymphoma Mouse Plasma

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Figure 5. MS/MS spectrum of 2 unique peptides derived from melanoma antigen, family B (MAGE-B4) from RcsX-tumor-bearing mouse plasma by Spectrum mill. A: Spectrum shows the fragment ions from a doubly charged precursor ion m/z 504.7 of “VTLVDSSCK” peptide (Score, 13.43; SPI, 83.8%) and B: Spectrum shows the fragment ions from a triply charged precursor ion m/z 616.9 of “EANSDPPSFEFLWGPR” peptide (Score, 12.20; SPI, 71.9%).

FEFLWGPR (Score, 12.20; SPI, 71.9%) 4 MS/MS spectra, only in tumor-bearing mouse plasma (Figure 5). To date, several MAGE proteins have been identified in a wide variety of malignant tumors but not in normal tissues except the testis. Particularly, expression of MAGE-B4 is restricted to the cytoplasm of fetal and adult gonads of most of the mammalian species including human and mouse,61 and there are no reports of the presence of MAGE proteins in serum or plasma. Although MAGE proteins have been used as targets for cancer immunotherapy,62 their function in normal cells remains unknown. In the present study, we detected MAGE-B4 protein in the tumor-bearing mouse plasma but none in normal mouse or normal human plasma. This was confirmed by Western blot analysis with rabbit polyclonal anti-human MAGE-B4 antibody using normal (3n) and tumor-bearing mouse (3n) plasma. Two MAGE-B4 positive bands at ∼100 kDa and 80 kDa were found in all three tumor-bearing mouse plasma samples but not in normal mouse, rat or human plasma (Figure 6B). Osterlund et al., reported only one band at ∼80 kDa in mouse testis.61 The ∼100 kDa band may represent extensive post-translational modification or it may be a secreted form of MAGE-B4. If its presence in plasma is confirmed it could be considered a candidate biomarker in this tumor model.

Conclusions Our results show that a 1D-Gel-LC-MS/MS-based approach is an efficient global method for comparison of the proteomes of body fluids, from different stages of disease, in animal models. This method also has potential for quantitation using ICAT reagents, as reported by Li et al.63 Our goalsvia generation

Figure 6. Immunoblot analysis of haptoglobin and MAGE-B4 proteins in mouse plasma, before and after RcsX-tumor growth in SJL mouse. A: Whole plasma (2 µL) from three SJL mice (lanes 1-3), one C57BL/6 (lane 4) and three RcsX-tumor-bearing SJL mice (lane7-9) was separated by SDS-PAGE and transferred to poly(vinylidene difluoride). The membrane was probed with anti-rat haptoglobin antibody. Rat (lane 5) and human (lane 6) plasma was used as positive controls. For the normal SJL and C57BL/6 mouse, 2 µL and for rat, human, and RcsX-tumor-bearing mouse, 0.25 µL plasma was loaded onto the gel. Figure 6B Plasma (2 uL) from three SJL mice (lanes 1-3), and three RcsXtumor-bearing SJL mice (lanes 4-6) was separated by SDS-PAGE and transferred to poly(vinylidene difluoride). The membrane was probed with anti-human MAGE-B4 antibody. Rat (lane7) and human (lane 8) plasma was used as negative controls. Arrows on the right indicate the MAGE-B4 bands.

of protein profiles in normal and experimental mouse plasmas was to find a potential diagnostic marker for RcsX-tumor growth in SJL mice. This work was facilitated by a new MS/MS data analysis toolsSpectrum Millswhich, among other features, allows semiquantitative comparison of protein concentrations between the samples. Journal of Proteome Research • Vol. 4, No. 5, 2005 1823

research articles To our knowledge, this is the first comprehensive report on the mouse plasma proteome and on a comparison of normal mouse plasma with RcsX lymphoma mouse plasma. After depletion of albumin and immunoglobulin, we identified 1079 mouse NR proteins in SJL mouse plasma in a single experiment. We expect, however, that further improvements can be achieved, e.g., by pre-fractionation of samples with MW cutoff filters to identify lower-molecular-weight plasma proteins. Probably due to the different physiological states of the animals (normal vs tumor-bearing), we found only 191 proteins in common. A number of proteins, whose levels were up-regulated in association with RcsX tumor growth in SJL mice, have been identified, some of which may play a role during the inflammatory response. We also identified several unique high-abundance proteins in tumor-bearing mouse plasma. Preliminary semiquantitative results suggest that a number of changes in protein concentrations can be associated with tumor growth. Morequantitative proteomics with isotopomeric reagents should allow identification of proteins in multiple samples and validation of potential biomarkers.

Acknowledgment. This work was supported by NCI (Grant No. P01-CA26731) and the MIT Center for Environmental Health Sciences (NIEHS Grant No. P30-ES02109). Thanks to Agilent for access to the MSD-Trap. We thank Laura Trudel for providing plasma samples and Karl R. Clauser for helpful discussions on Spectrum Mill data analysis. Supporting Information Available: Supporting Table containing the list of common proteins found in normal and RcsX-tumor-bearing SJL mouse plasma with classification according to their subcellular localization and biological function. References (1) Murphy, E. D. Proc. Am. Assoc. Cancer Res. 1963, 4, 46. (2) Tsiagbe, V. K.; Thorbecke, G. J. Cell Immunol. 1990, 129, 494502. (3) Weller, A. H.; Magliato, S. A.; Bell, K. P.; Rosenberg, N. L. Muscle Nerve 1997, 20, 72-82. (4) Kostek, C. A.; Dominov, J. A.; Miller, J. B. Am. J Pathol. 2002, 160, 833-839. (5) Tamir, S.; Gal, A.; Weller, A. H.; Liang, W.; Fox, J. G.; Wogan, G. N.; Tannenbaum, S. R. Cancer Res. 1995, 55, 4391-4397. (6) Nakano, K.; Cinader, B. Eur. J Immunol. 1980, 10, 309-316. (7) Haran-Ghera, N.; Kotler, M.; Meshorer, A. J. Natl. Cancer Inst. 1967, 39, 653-661. (8) Tsiagbe, V. K.; Yoshimoto, T.; Asakawa, J.; Cho, S. Y.; Meruelo, D.; Thorbecke, G. J. EMBO J. 1993, 12, 2313-2320. (9) Ponzio, N. M.; Hayama, T.; Nagler, C.; Katz, I. R.; Hoffmann, M. K.; Gilbert, K.; Vilcek, J.; Thorbecke, G. J. J. Natl. Cancer Inst. 1984, 72, 311-320. (10) Katz, I. R.; Chapman-Alexander, J.; Jacobson, E. B.; Lerman, S. P.; Thorbecke, G. J. Cell Immunol. 1981, 65, 84-92. (11) Lasky, J. L.; Ponzio, N. M.; Thorbecke, G. J. J. Immunol. 1988, 140, 679-687. (12) Gal, A.; Tamir, S.; Tannenbaum, S. R.; Wogan, G. N. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 11499-11503. (13) Gal, A.; Tamir, S.; Kennedy, L. J.; Tannenbaum, S. R.; Wogan, G. N. Cancer Res. 1997, 57, 1823-1828. (14) Mannick, E. E.; Bravo, L. E.; Zarama, G.; Realpe, J. L.; Zhang, X. J.; Ruiz, B.; Fontham, E. T.; Mera, R.; Miller, M. J.; Correa, P. CANCER RES. 1996, 56, 3238-3243. (15) Dedon, P. C.; Tannenbaum, S. R. Arch. Biochem. Biophys. 2004, 423, 12-22. (16) Jaiswal, M.; LaRusso, N. F.; Gores, G. J. Am. J. Physiol. Gastrointest. Liver Physiol. 2001, 281, G626-G634. (17) Gal, A.; Wogan, G. N. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 1510215107. (18) Ebersole, J. L.; Cappelli, D. Periodontol. 2000. 2000, 23, 19-49.

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