Dissection of DEN-Induced Platelet Proteome Changes Reveals the Progressively Dys-Regulated Pathways Indicative of Hepatocarcinogenesis Taohua Leng,†,‡,§ Na Liu,†,‡ Ying Dai,‡ Yanbao Yu,‡ Chen Zhang,‡,§ Ruyun Du,‡,§ and Xian Chen*,‡,§,| Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, People’s Republic of China, and Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States Received May 8, 2010
Due to the lack of precise markers indicative of its occurrence, progression, and malignant stages, hepatocellular carcinoma (HCC) is currently associated with high mortality. Given the fact that thrombocytopenia is associated with chronic liver diseases, and the multifunctional nature of platelets we reason that phenotype-specific platelets could be the systemic barometer for hepato-carcinogenesis. The mass spectrometry (MS)-based proteomic efforts to discover novel biomarkers in plasma or serum are largely compromised by a few of the overwhelmingly abundant proteins that comprise over 95% of the total protein mass of plasma or sera. Platelets however are free of these MS signal-suppressing proteins. On the basis of a HCC animal model where diethyl nitrosamine (DEN) administration on male rats specifically induces HCC, by using a multiplex quantitative proteomic approach, we profiled the phase-to-phase proteome changes in a series of viable phenotype-specific platelets along with the DENinduced progressive liver transformation. The platelet proteome was found highly responsive to each physiological stage of liver inflammation or pathogenesis. Using data-dependent bioinformatics network analysis, we found that certain pathway modules involved in immune response, tissue wound repair, apoptosis, cell proliferation, and catabolism and metabolism were differentially regulated, which were uncovered by the DEN-induced differential expression of the corresponding pathway components. The phase-specific presentations of these pathways suggested that the DEN-induced progression of immune suppression and apoptosis resistance is dynamically coordinated in the platelets. These novel platelet signatures are interconnected in the dynamic networks along with HCC progression and could be identified noninvasively for HCC prognosis and early diagnosis. Keywords: hepatocellular carcinoma • platelet proteome • dys-regulated pathways
Introduction Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors worldwide.1 Due to the largely unknown mechanism of hepatocellular carcinogenesis and therefore the lack of prognostic markers for its progression, most of the HCC cases could only be diagnosed decisively at their late stages. Mass spectrometry (MS)-based proteomic screening of the serum or plasma samples associated with various pathological phenotypes has been the major focus to identify novel biomarkers for noninvasive diagnosis of cancers.2 However, little progress has been made due to the existence of highly * To whom correspondence should be addressed. Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, and Institutes of Biomedical Sciences, Fudan University, No. 138, Yixueyuan road, Shanghai, 200032 China. Phone: 919- 843-5310. Fax: 919-966-2852. E-mail:
[email protected]. † These authors contributed equally to this study. ‡ Department of Chemistry, Fudan University. § Institutes of Biomedical Sciences, Fudan University. | University of North Carolina at Chapel Hill. 10.1021/pr100679t
2010 American Chemical Society
abundant proteins such as albumin and immunoglobulin, which could suppress the MS signals of many possible clinical markers, that is, signaling or regulatory proteins, cytokines, etc., presenting in much lower abundance. Affinity depletion of highly abundant proteins is now commonly used to increase the dynamic range of MS detection, which however may result in the loss of the proteins of physiological importance sticking with high-abundance proteins. In human, blood platelets are the second most numerous cells responsible for the maintenance of vascular integrity.3 The platelet proteome, on the other hand, is free from major highly abundant proteins as it is easily accessible for detecting possible biomarkers by MS in a noninvasive way.4 Platelets are among the largely under-studied cells in liver tumorigenesis, yet clinical evidence indicates that thrombocytopenia or low platelet counts is a common complication in patients with chronic liver diseases, suggesting that human platelets are highly responsive to liver pathogenesis.5 In addition to the functional roles already known for platelets,3 emerging evidence implies that Journal of Proteome Research 2010, 9, 6207–6219 6207 Published on Web 10/04/2010
research articles
Leng et al.
Figure 1. Definition of DEN-induced pathologic phases (Phase I-V) and iTRAQ-based quantitative proteomic design. (A) Rat platelets and liver were collected at the 30th day after DEN administration via drinking water (Phase I), at the 30th day after phase I drinking with no DEN containing water (Phase II), at the 30th day after phase II with resumed DEN administration (Phase III), at the 60th day after phase II with resumed DEN administration (Phase IV), and at the 90th day after phase II with resumed DEN administration (Phase V). (B) Workflow of sample labeling with iTRAQ reagents. “T- Control” stands for the protein extract of the control platelets from nonexposed rats; “T+ control” defines the sample from thrombin-stimulated control platelets; “T- Phase N” is for the platelet protein sample from each phase; and “T+ Phase N” defines for thrombin-stimulated “T- Phase N”.
platelets also release inflammatory and mitogenic substances that promote a variety of biological processes including wound healing, tissue repair, angiogenesis, and apoptosis;6-8 for example, liver regeneration is also significantly impaired in platelet-depleted animals.6 Considering that tumor growth and metastasis are angiogenesis-dependent, platelet-associated factor 4 (PF4) was identified as an inhibitor of angiogenesis for marking early tumor appearance.9 Platelets also express a series of adhesion molecules including P-selectin and β3 integrins, which enable them to interact with leukocytes, monocytes, etc.8 Similar to other types of immune cells, platelets express tolllike receptors (TLRs) that promote platelet binding to neutrophils, leading to the rapid formation of neutrophil extracellular traps to entrap bacteria.10 Platelets are the rich source of cytokines and chemokines,8 which are released upon platelet activation.11 Although platelets are enucleated cells, activated platelets also express phosphatidylserine,7 cytochrome C, caspases, Bcl-2, etc.,12 which are known as the hallmarks of apoptosis in all nucleated cells. A chemical carcinogen, diethyl nitrosamine (DEN), can specifically induce HCC in mice or rats.13 Furthermore, previous studies using comparative functional genomics indicated that the expression patterns in DEN-induced mouse HCCs were most similar to those of predefined subclasses of human HCCs.13 Considering that platelets are multifunctional and could be indicative of the progression of chronic liver diseases, we extended the use of the isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic approach to measure the progressive changes in the rat platelet proteome corresponding to the DEN-induced liver pathological changes. The iTRAQ-based strategy is multiplex, allowing the quantitative analysis of not only phase-to-control but also 6208
Journal of Proteome Research • Vol. 9, No. 12, 2010
phase-to-phase differentially expressed/secreted proteins in a single data set with high data consistency. Using bioinformatics tools, these proteins showing phase-to-phase changes were clustered in corresponding functional pathways or networks, which could lead to identification of those platelet markers responsive to the DEN-induced liver tissue transformation to HCC and the pathways they are involved in.
Experimental Procedures DEN Administration and Sample Collection. Six week-old male rats (Wistar strain from the Animal Resources Centre, Shanghai Medical School, Shanghai, China) weighing 130 ( 20 g were housed in laminar-flow cabinets under specific pathogen-free conditions. Twenty rats were used as the control. One-hundred rats were administered with 95 µg/mL DEN (Sigma, St Louis, MO) in the drinking water for 30 days. The water without DEN was then given to these rats for another 30 days. These rats were then continuously subjected to 95 µg/ mL DEN administration via drinking water for 70 days. Thereafter pure drinking water was given for another 20 days for their continuous survival. Considering the death rate during DEN-induced tumor progression, 15 rats were used in each early phase. Fourteen rats died in the last phase as a result of cancer. Along with nontreated rats, platelets, liver tissue, and plasma were collected every 30 days at 4 different phases during DEN-induced tumor progression (Figure 1A). Platelet Protein Extraction, Digestion, and iTRAQ-Labeling. After narcosis with sodium pentobarbital by intraperitoneal injection, the thorax of rats was opened to collect whole blood from the heart. The fresh blood was transferred into EDTA anticoagulant tubes and viable platelets were obtained through
Platelet Pathways Dys-Regulated by Diethyl Nitrosamine multistep centrifugation and were purified as previously described.14 Briefly, after centrifugation at 200× g for 20 min, the upper two-thirds of the platelet-rich plasma (PRP) was carefully collected, pooled, and followed by 12 min centrifugation at 700× g. The platelet pellets were resuspended in Tyrodes buffer (2.7 mM KCl, 137 mM NaCl, 10 mM Hepes, 1 mM EDTA, 5 mM D-glucose, pH 7.4) and washing buffer (36 mM sodium citrate, 5 mM KCl, 90 mM NaCl, 10 mM EDTA, 5 mM D-glucose) based on the procedures described previously.14 After 1 h culturing at room temperature, the platelets were spun down again at 700× g for 12 min. The platelet pellets were resuspended in 2 mL Tyrodes buffer to a concentration of 1 × 109 cells/mL. Half of the platelets were stimulated by the addition of 100 µL stimulation solution (1 U thrombin, 20 mM CaCl2, 20 mM MgCl2 in Tyrodes buffer) for 3 min at 37 °C. Platelets were pelleted at 5000× g for 3 min and the supernatant was removed. The combined platelet pellets were lysed in a lysis buffer containing 8 M urea and 1% (v/v) protease/phosphatase inhibitor cocktails (Sigma) for 30 min on ice, and then the lysate was centrifuged at 15 000 rpm for 15 min and the supernatant was transferred into a new EP tubes. The protein mixture (100 µg) extracted from each platelet phase was reduced with TCEP, cysteine-blocked by treatment with iodoacetamide, digested with trypsin, diluted with the dissolution buffer (50 mM triethylamine), and labeled with an isobaric tag reagent by following the manufacturer’s protocol (Applied Biosystems, Foster City, CA). The human platelet samples from HCC male patients were obtained and then pooled from six volunteers aged 40-70 years with their consent at Fudan University Zhongshan Hospital (Shanghai) following the protocols described earlier.14 Similarly, six healthy human platelets were obtained and pooled from male healthy donors aged 40-70 years with consent who took no medications for at least 2 weeks prior to the donation. These procedures were performed under the guideline/regulation approval by the IRB committee in ZhongShan Hospital of Fudan University. Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Analysis. Four sets of platelet peptides, labeled with each isotope version of iTRAQ reagents, were mixed at equal mass ratio, the solution was adjusted to pH 3, and the peptide mixture was injected at a flow rate of 50 µL/min into a SCX column (50-µm particle size, 4.0 mm × 15 mm). After washing excess TCEP, SDS, calcium chloride, and iTRAQ reagents, the peptides were separated into 10 fractions using 200 µL elution buffer of 10 mM KH2PO4 in 25% acetonitrile with multiple KCl salt steps (25/37.5/50/75/100/125/150/175/200/ 350 mM KCl) at pH 3.0. The eluted peptides at each salt step were collected and lyophilized. Each SCX fraction was redissolved in 20 µL of buffer A (0.1% formic acid and 5% acetonitrile). Ten microliters of peptide mixtures were separated by a 120 min gradient for LC-MS/ MS via a 75 µm × 15 cm C18 PepMap capillary column on a nanoflow LC Packing System (Dionex) interfaced with a ESI-MS/MS instrument (QSTAR Elite). Data-dependent MS and MS/MS acquisitions were made using a 1 s survey scan from m/z 400-1600 followed by 3 precursors selected for MS/ MS from m/z 100-2000 using dynamic exclusion, and the rolling collision energy was used to promote fragmentation. Every third scan the peak that was closest in intensity to the threshold of 10 counts was selected for MS/MS. Data acquisition was performed without any repetitions and with a dynamic exclusion of 30 s.
research articles ProteinPilot version 2.0 software with the Paragon Algorithm (Applied Biosystem) was used for the identification and quantification of the relative abundance of platelet proteins. Data searches were performed against the NCBI rat protein database (NCBInr_060227_tdr.fasta, 56 824 entries) using default settings: 95% confidence with at least two unique peptides for protein identification, trypsin cleavage specificity, methyl methanethiosulfonate (MMTS) as the defined modification, and biological modification identifier settings for sequence identification. This software calculates a percentage of confidence that reflects the probability that the hit is a false positive so that at the 95% confidence level there is a false positive identification rate of around 5%.15 For protein relative quantification, only MS/MS spectra that were unique to a particular protein and where the sum of the signal-to-noise ratio for all of the peak pairs was >9 were used for quantification(default software settings).The majority of averaged iTRAQ protein ratios reported by ProteinPilot have a p-value (evaluating the statistical difference between the observed ratio and unity) and EF (error factor) for each hit of protein identification with an Unused score g1.3 or 95% confidence. The EF term indicates the actual average value lies between (reported ratio)/(EF) and (reported ratio) × (EF) at a 95% confidence. Only those protein matches with a meaningful EF (2) was manually checked for the iTRAQ ratio of its individual peptides(Supplemental Tables 9 and 10, Supporting Information). The iTRAQ-based quantitation could also be done for proteins with a single unique peptide of 99% confidence by using ProteinPilot. Functional Categorization by Database for Annotation, Visualization, and Integrated Discovery (DAVID).16 A list of gene identifiers in the form of Genbank accession numbers, Entrez Gene identifiers, GI_ accessions, etc. were submitted to DAVID (http://david.abcc.ncifcrf.gov/) for the cluster analysis of functional annotation, pathways, protein domains, KEGG pathway, and protein-protein interactions. Histology Analysis of DEN-Induced Liver Transformation.17 The rat livers at different pathological phases were sliced, washed with PBS, fixed with 10% phosphate buffered formalin, dehydrated, and embedded in paraffin. Each tissue slice with 5 µm in thickness was stained with Hematoxylin and Eosin (H&E) for pathological characterization by using light microscopy. Histological changes in the liver were assessed according to the degree of hepatic cell necrosis, lymphocyte infiltration, fibroplastic proliferation, bile duct proliferation, and angiogenesis. Western Blotting and Enzyme-Linked Immuno-Assay. Immunoblotting experiments were performed with 1:1000 dilution of the corresponding antibody for P-selectin (Biovision, 3633R100), RANTES (eBioscience, 14-7993), SERBP1 (Abcam, ab55994), GAPDH (Kangcheng, KC-5G4), and Beta-Actin (Kangcheng, KC5A08). The phase-specific concentrations of soluble P-selectin in plasma were measured by ELISA using a commercially available rat P-selectin ELISA kit following the manufacture’s protocol (Xitang, F1680). Network Analysis using STRING (http://string.embl.de/). STRING is a database of known and predicted protein-protein interactions that integrates the information about physical and functional protein associations from numerous sources including experimental repositories, computational prediction methods, and public text collections. For those phase-specific proteins identified, we uploaded their gene accession numbers or protein names onto the STRING database. Journal of Proteome Research • Vol. 9, No. 12, 2010 6209
research articles
Leng et al.
Figure 2. Immunoblotting validation of the accuracy of iTRAQ-based quantitation. The selected proteins include P-selectin or SELP, PAI-mRNA binding protein (SERBP1), and Regulated on Activation Normal T cell Expressed and Secreted (RANTES or CCL5) while β-actin and GAPDH were used as the loading control. Western blotting experiments were performed on (A) the platelet protein mixtures extracted from the nonexposed and phase I to phase V and (B) the human platelets isolated from healthy donors and HCC patients.
Results and Discussions iTRAQ-Based Quantitative Analysis Identified the DENInduced Phase-to-Phase Platelet Proteome Changes. The overall design for sample collection was given in Figure 1A. In addition to the nonstimulated control, the DEN-induced liver pathology processes from mild inflammation (Phase I), to moderate inflammation (Phase II), to early cirrhosis (Phase III), to cirrhosis (Phase IV), and then to HCC (Phase V) (Supplemental Figure 1, Supporting Information). Accordingly, we found that the expression level of soluble P-selectin in plasma was dramatically increased in the first three phases, and then gradually decreased in the last two phases, indicating the progressive changes in the activation states of platelets during DEN-induced HCC progression (Supplemental Figure 2, Supporting Information). In each of the five 4-plex iTRAQ experimental sets containing the platelets collected from the rats at different pathological phases (Figure 1B), we included the same pair of nonstimulated (T- control) and thrombin-stimulated (T+ control) platelets isolated from non-DEN exposed rats as the run-to-run control. Thus, we were able not only to minimize the run-to-run variations in the sample processing including tryptic digestion, iTRAQ tag labeling, SCX fractionation, and RPLC-MS/MS but also to perform cross-phase quantitative analysis of differentially expressed proteins. Also a study was performed to evaluate possible platelet protein variability associated with individual genetic background where the platelet protein variations were examined in 20 individuals aged 56-100 years, and were found only at 18%.4 Due to this naturally low variation in the platelet proteome from diverse genetic backgrounds, our comparison of the healthy versus phase-specific mouse pairs at different ages and the combination of the samples from a group of rats at defined pathological phases is valid for the abundance-based mass spectrometric analysis. As a result, we identified 437, 333, 456, 547, and 424 nonredundant proteins in high confidence in each of the iTRAQ-based LC-MS/MS runs respectively (Supplemental Table 1, Supporting Information). Most of proteins matched with one peptide in our list were also supported by other MS/ MS spectra with relative low scores or non-iTRAQ labeling. The highly abundant proteins in serum or plasma were not detected as the major species, suggesting the high purity of the platelet 6210
Journal of Proteome Research • Vol. 9, No. 12, 2010
isolation. Among them, 204 proteins were identified and quantified across all five phases. Except for 329 proteins identified in only a single experimental run, more than 85% of the quantified proteins appearing in at least two iTRAQ runs had the iTRAQ ratios with standard deviation (SD) less than 0.2. 10% of the quantified proteins had a SD between 0.2 and 0.3, and only few proteins had SD higher than 0.3 (Supplemental Table 2, Supporting Information). On the basis of the relatively low run-to-run SD in our data set, we considered any change more than 20% in peptide abundance as the threshold to define those phase-to-phase differentially expressed proteins.18 Quantitative Accuracy and Reproducibility of iTRAQ Quantitative Results were Validated with Rat and Human Clinic Samples. In our validation experiments on the same set of phase-specific mouse platelet samples using immunoblotting, the platelet-characteristic proteins such as P-selectin (SELP), RANTES or CCL5, and SERBP1, showed the similar trend of DEN-induced phase-to-phase changes (Figure 2A) to what measured by quantitative proteomics (Supplemental Tables 4 and 6, Supporting Information). Also, we compared their changes in the paired human platelets isolated from healthy donors and HCC patients. The HCC patients were defined at HCC stage III according to CUPI staging system which approximately corresponds to the phase V rats. As shown in Figure 2B, the consistency between iTRAQ-based quantitation and immunoblotting for both rat and human samples demonstrates the clinical accuracy of our quantitative proteomic data set, which lies down a foundation to perform the following data-dependent analysis for the functional implications of the proteomic discovery. Distribution of the Identified Platelet Proteins in Different Function Categories. By using both DAVID and KEGG pathway bioinformatics tools, we sorted out the function clusters for those proteins showing the phase-to-control or phase-to-phase abundance changes. As most of the proteins detected were functionally related to cytoskeleton organization, biogenesis, protein transport, localization, and metabolic processes, etc. (Figure 3A), and many proteins found to undergo phase-tophase changes in their abundance are involved in regulation of tissue wound repair, immune and inflammatory responses, complement and coagulation cascades, antigen processing and
Platelet Pathways Dys-Regulated by Diethyl Nitrosamine
research articles
Figure 3. Distribution of the proteins quantified by iTRAQ-based approach in different function categories and biological processes. The radar plot of the distribution of the quantified proteins in (A) different function categories and biological processes and (B) major KEGG pathways involved in tumor progression.
presentation, apoptosis, and cell proliferation (Figure 3B, Supplemental Figure 3, and Supplemental Table 3, Supporting Information). Major Functional Pathways Found to be Activated in the Platelets at the Early Phases. At the phase I platelets, we identified 139 up-regulated and 140 down-regulated proteins compared to their abundances in untreated rats. We found that most of the up-regulated proteins were those primarily involved in tissue wound repair, antigen processing/presenting, complement and coagulation cascades, cell proliferation, and apoptosis. For example, 17 proteins known to be associated with tissue wound repair, such as serine protease inhibitors, complement family proteins, fibrinogen, coagulation factors, and lipid family proteins, were all up-regulated (Supplemental Table 4, Supporting Information). Related to immune/inflammatory response, 16 proteins were up-regulated such as pro-platelet basic protein (PPBP/CXCL7), and Ba2-693 (CRP), respectively, while no other proteins in this functional category were found down-regulated (Supplemental Table 4, Supporting Information). In the complement and coagulation cascades which contain cofactors for tissue wound repair and inflammatory response, we also found 8 up-regulated proteins. Meanwhile, 4 up-regulated proteins associated with antigen processing/ presentation were identified (Supplemental Table 3, Supporting
Information). Further, 16 cell proliferation-related proteins were found up-regulated, such as integrin linked kinase (ILK), rous sarcoma oncogene (SRC), and kininogen 1 (Supplemental Table 5, Supporting Information). ILK is a serine/threonine kinase and scaffolding protein known to modulate the activity of its downstream targets for cancer cell growth and survival.19 SRC is a proto-oncogene that could suppress SHPS-1 expression via the Ras-MAP kinase pathway to promote the oncogenic growth of cells.20 We also identified 15 up-regulated proteins previously known to associate with cell apoptosis, including tissue inhibitor of metalloproteinase 3 (TIMP3), and programmed cell death 5 (PDCD5) (Supplemental Table 6, Supporting Information). Meanwhile, 5 antiapoptotic proteins, including cofilin 1 (CFL1), peroxiredoxin 2 (PRDX2), thioredoxin domain containing protein 5 precursor (TXNDC5), were found down-regulated. These observed changes in the platelet proteome were consistent with that TIMP3 overexpression induces a Fas-associated death domain-dependent type II apoptotic pathway,21 while PDCD5 promotes the activation of caspase-3 and the release of cytochrome c, regulating the cells undergoing apoptosis.22 To further distinguish those platelet proteins that are specifically regulated during liver regeneration/tissue wound repair, we suspended the oral DEN administration for 30 days (Phase II). Compared to the healthy platelet proteome, 120 proteins Journal of Proteome Research • Vol. 9, No. 12, 2010 6211
research articles were found up-regulated at phase II with the majority of them related to tissue wound repair and immune/inflammatory response including src-associated phosphoprotein 2 (SCAP2), lymphocyte cytosolic protein 2 (LCP2) (Supplemental Table 4, Supporting Information), suggesting phase II-specific activation of platelets for mediating tissue wound repair. Meanwhile, we also found 9 proteins involved in the complement and coagulation cascades were up-regulated (Supplemental Table 3, Supporting Information) including fibrinogen which is related to blood clot formation,23 coagulation factor and plasminogen both are also known to promote fibrin formation and to generate thrombin to activate platelet aggregation.24,25 Importantly, most of these proteins showed higher abundance increases at phase II than those at phase I (Supplemental Table 4, Supporting Information), suggesting the pathways associated with tissue wound repair were more active in the phase II platelets. At phase II, certain proteins associated with cell apoptosis such as tetratricopeptide repeat domain 11 (FIS1) and PDCD5 were also identified with about 2-fold expression increases (Supplemental Table 6, Supporting Information). FIS1 is known to be localized in the mitochondrial outer membrane and is a part of the mammalian fission machinery related to apoptosis.26 On the other hand, we also identified several apoptosisrelated proteins down-regulated, suggesting a possible phasespecific balance between mediating the apoptosis of impaired platelets and protecting functional platelets from apoptosis. In this regard, in a cross-phase quantitative comparison (Supplemental Table 6, Supporting Information), the number of upregulated proteins involved in cell apoptosis in phase I was obviously more than that in phase II, while the number of down-regulated proteins in this category in phase I was less than that in phase II, suggesting an overpresentation of apoptosis-related pathways in the phase I platelets. In these early phases corresponding to the DEN-induced acute inflammation in the liver (Figure 1), a broad activation of multiple pathways in the platelets represented by their differentially expressed pathway components was observed for mediating tissue wound repair, immune and inflammatory response, apoptosis, and cell proliferation, suggesting the ability of activated platelets in coordinating multiple programs for protecting healthy cells, repairing wound tissues, and promoting the apoptosis of damaged tissue cells (Supplemental Table 3, Supporting Information). Impaired Pathways Associated with Immune Response and Apoptosis in Platelets were in Coordination with the DEN-Induced Progressive Liver Cirrhosis. A total of 139 proteins were found differentially expressed at phase III corresponding to early liver cirrhosis (Supplemental Figure 1, Supporting Information). Interestingly, 15 proteins associated with host defense and immune response such as MHC class 1b antigen (MHC-1b) and complement component 4a (C4A) were down-regulated (Supplemental Table 4, Supporting Information). The total number of down-regulated proteins involved in the immune response was more than that of upregulated proteins. Simultaneously, we found a few proteins related to apoptosis were down-regulated, represented by Bcl2associated X protein (BAX) and tumor rejection antigen gp96 (TRA1) (Supplemental Table 6, Supporting Information). BAX is well-known as a target gene of p53 in promoting apoptosis.27 TRA1 is a specific substrate for calpain cleavage concurrent with the progression of apoptosis induced by DNA damage.28 Thus, 6212
Journal of Proteome Research • Vol. 9, No. 12, 2010
Leng et al. the down-regulation of apoptotic promoters suggested the apoptosis began to be suppressed in the platelets at phase III. At phase IV, 217 up-regulated and 212 down-regulated platelet proteins were found with respect to their abundances in normal platelets. 35 proteins primarily involved in the signaling for immune responses were down-regulated, represented by p38 mitogen activated protein kinase 14 (MAPK14), serine/threonine kinase 4 (STK4) and protein kinase C substrate 80K-H (PRKCSH), complement family proteins C3 and C9, complement component C1q receptor precursor (C1qRp) and complement receptor related protein isoform 2 (CRRY) (Supplemental Table 4, Supporting Information). Only 13 up-regulated proteins were observed in this category. Meanwhile, 8 proteins involved in antigen processing and presentation were found down-regulated (Supplemental Table 3, Supporting Information), all indicating more suppression of the platelets in mediating immune response. Meanwhile, 26 proteins known as pro-apoptosis regulators were found down-regulated (Supplemental Table 6, Supporting Information), such as caspase 3, reticulon 4 (RTN4/NOGO), HLA-B-associated transcript 3 isoform 1 (BAT3), etc. An overexpression of RTN4 could induce apoptosis in various cancer cells.29 BAT3 is a gene required for p53 transcriptional activity and p300-mediated p53 acetylation for apoptosis in response to genotoxic stress.30 The simultaneous down-regulation of these proteins suggested the developing resistance for apoptosis at phase IV. In reviewing the characteristics of up- or down-regulated platelet proteins corresponding to the phase III and VI liver tissues, the total number of down-regulated proteins associated with the host defense and immune response was much higher than that of up-regulated proteins, suggesting that the overall platelet-mediated immune response was becoming suppressed, and was closely associated with the development and the severity of liver fibrosis and cirrhosis. Meanwhile, for the proteins associated with apoptosis, 7 proteins were found down-regulated while only 2 up-regulated proteins were identified in the phase III (Supplemental Table 6, Supporting Information), indicating a progressive suppression of apoptosis pathways. A Broader Scale Suppression of Immune Response and Apoptosis was Found in the Platelet Proteome Corresponding to the DEN-Induced HCC. At the HCC-developed phases (Phase IV and V), the proteins involved in the wound repair and immune response were all found significantly down-regulated, represented by RANTES, fibrinogen, coagulator factors, lipid family proteins, and TGF-β1, while no up-regulated proteins in these categories were detected (Figure 2A and Supplemental Table 4, Supporting Information). Furthermore, in response to tissue damage the function of platelets is known to be strongly influenced by their interactions with the surrounding extracellular matrix (ECM) which may facilitate their rolling, adhesion and migration through the vessel wall into the damage tissue. Here, we found that 5 proteins involved in complement and coagulation cascades and 6 proteins known to participate in ECM-receptor pathway were all down-regulated (Supplemental Table 3, Supporting Information). In the cell proliferation category (Supplemental Table 5, Supporting Information), 12 proteins were down-regulated, such as ILK, and TGFB1, along with only two up-regulated proteins. In addition, we also found that 16 pro-apoptosis proteins such as SH3-domain kinase binding protein 1 (SH3KBP1), prohibitin (PHB), and calreticulin (CALR) were all down-regulated, and 4 antiapoptotic proteins such as pro-
Platelet Pathways Dys-Regulated by Diethyl Nitrosamine grammed cell death 6 interacting protein (PDCD6IP) were upregulated (Supplemental Table 6, Supporting Information). SH3KBP1 is a multidomain protein associated with TNFR1 via Src for modulating TNFR-induced apoptosis.31 CALR was found as an apoptosis promoter.32 On the other hand, PDCD6IP is an adaptor protein binding to a calcium-binding protein ALG-2 (apoptosis-linked gene 2).33 The large number of the downregulated proteins involved in tissue wound repair, host defense, cell proliferation, and apoptosis predominately found in phase V suggested a DEN-induced broad-scale suppression of these biological processes. Meanwhile the resistance toward apoptosis appeared more pronounced in the platelets along with HCC progression. DEN Progressively Induces the Pathway-Scale Differential Regulation. We then clustered those platelet proteins showing phase-to-phase changes based on their known associations with particular pathways. For example, similar to what was observed for P-selectin, the expression of a wound repairassociating protein ITGB38 was up-regulated by 1.3-fold in phase II and then down-regulated by 0.62-fold, 0.25-fold, and 0.47-fold in phases III-V, respectively (Figure 4A, Supplemental Table 4, Supporting Information). Plasminogen (PLG) was upregulated by 1.5- and 1.3-fold at phases II and IV respectively, and down-regulated by 0.56-fold at phase V (Supplemental Table 4, Supporting Information). As shown in Figure 4B for this wound repair category, the overall number of up-regulated proteins was found to be greater than that of down-regulated proteins in phase I and II, while an opposite trend of protein regulation with more down-regulated proteins was observed in the phase V platelet proteome (Supplemental Table 4, Supporting Information). Represented by complement components such as C4A, C9, and LCP2 which showed the phasedependent ‘up-to-down’ changes (Figure 4C), most of the proteins known to be involved in the immune and inflammatory response were significantly up-regulated in the first 60 days after DEN administration, and then the level of their activation was gradually suppressed with the continuous DEN treatment (Figure 4C and Supplemental Figure 3, Supporting Information). Represented by CALR, ALOX12, and SPIN2B (Figure 4E), more cell apoptosis-related proteins were found up-regulated proteins in the phase I platelets, while more down-regulated proteins in the same category were observed starting from phase III. Specifically, in the platelet proteome with the HCCdeveloped liver (Phase V) many down-regulated proteins associated with apoptosis were found along with several upregulated proteins related to antiapoptosis (Figure 4F). Several proteins related to the mitochondria-mediated apoptosis such as BAX, caspase 3, PYCARD, BAT3, RTN4, TIMP3, and CALR were found differentially regulated in the phase-specific platelets, suggesting that similar apoptotic pathways such as p53BAX mitochondria-mediated apoptosis both occurred in nucleated cells and in the platelets. By using the STRING mapping tool, as shown in Figure 5, we found that many proteins showing phase-to-phase DENinduced changes are interconnected in different pathway modules or regulatory networks associated with either complement and coagulation for immune response (Figure 5A), or apoptosis (Figure 5B), or antigen processing/presentation (Figure 5C), or tissue wound repair (Figure 5D), or cell proliferation (Figure 5E). At each pathological phase, simultaneous identification of those up- or down-regulated proteins in particular function clusters can distinguish those phase-
research articles specific overpresenting or repressing pathways. For example, in most of pathogenic phases we were able to cluster a large number of the proteins into the pathway modules associated either with complement and coagulation (immune response) (Figure 5A I-V), or with apoptosis (Figure 5B I-V) and found their showing phase-dependent differential expression with respect to the unstimulated control. Thereby, at the early phase of DEN induction both complement/coagulation and apoptosis are the overpresenting pathways, reflected by the up-regulation of the majority of the pathway components. Along with DENinduced tissue transformation (Supplemental Figure 1, Supporting Information), the overall pathways of complement and coagulation and apoptosis are progressively suppressed, suggested by the evidence of more and more down-regulating pathway components. Note that it is not uncommon that not all proteins could be identified and quantified in each individual phase by MS-based approaches. For the antigen processing/presentation pathway module we were only able to compare the differential regulation of those proteins identified in phases I and IV, which indicated the up-regulated antigen processing/presentation at early phase and the down-regulating same pathway in HCC progression. On a larger view as shown in Figure 6, our pathway mapping efforts identified 39 proteins with their known or extended links to multiple pathways of immune response, apoptosis, and tissue wound repair. On a broader view the DEN-induced progressive shifts in protein regulation suggested the cross talks among these pathway modules in coordinating the phasespecific platelet response to tissue transformation. Interestingly, in contrast to what we have observed (Figure 7), certain proteins associated with catabolism/glycolysis and metabolism were found showing the opposite trend of DEN-regulated phase-to-phase changes (Supplemental Table 7, Supporting Information). For example, lactate dehydrogenase A (LDHA), which is involved in anaerobic glycolysis, was previously reported up-regulated in both tumor tissue and the serum of cancer patients.34 We found this protein down-regulated by 0.7fold at phase II and then up-regulated by 1.3-fold and 1.6-fold at the phase IV and phase V, respectively (Supplemental Table 7, Supporting Information). Meanwhile, chaperonin subunits 3 and 4 were known as the up-regulated proteins in HCCs.35 Both proteins showed phase-dependent differential regulation as their expression levels remained unchanged at phase I, and became down-regulated at phase II, and then both were upregulated at phases IV and V, respectively (Supplemental Table 7, Supporting Information). In the pathway module as shown in Figure 7, alpha enolase (ENO1), a key enzyme in the glycolytic pathway which is up-regulated in the HCV-related HCC,36 was found down-regulated by 0.68 fold at both phase I and II, and then up-regulated by 2.0- and 1.2-fold at phase IV and V respectively (Supplemental Table 7, Supporting Information). Generally, the increased level of catabolism/ glycolysis represented an intracellular hallmarker of neoplasms, which enables cancer cells to survive.37 This result suggested that the activated catabolism/glycolysis and metabolism in platelets were corresponding with HCC progression. HCC represents a classic case of inflammation-linked cancer, and the lower platelet counts were observed in patients with chronic HCV or HBV infection or HCC, suggesting a close association between platelets and chronic liver inflammation.5 DEN-induced progression from acute to chronic inflammation in the liver is a major contributing factor to hepto-carcinogenesis.38 Under pro-inflammatory conditions, platelets localize Journal of Proteome Research • Vol. 9, No. 12, 2010 6213
research articles
Leng et al.
Figure 4. DEN-induced phase-to-phase quantitative changes of the proteins involved in tissue wound repair, immune response, and cell apoptosis. The DEN-induced trend of differential expression (left) and the phase-to-control ratios measured by iTRAQ-based method (right) were given for their representing proteins and total proteins involved in (A) and (B) tissue wound repair, (C) and (D) immune response, (E) and (F) apoptosis, respectively. The protein abbreviations are given in Supplemental Table 4 and Supplemental Table 6 (Supporting Information).
to lung and liver microvasculature to monitor and maintain the hemostasis of blood and the tissues. Thus, the platelet proteome contains the best protein representatives of the phenotype-specific liver conditions that could be biomarkers for early diagnosis and therapeutic intervention of chronic liver diseases including HCC. 6214
Journal of Proteome Research • Vol. 9, No. 12, 2010
Based on our observations, the signal suppression caused by fibronectin type of high abundant proteins in platelets was minimized in mass spectra in comparison with what albumin and IgGs in serum/plasma can introduce. Because we used iTRAQbased quantitative proteomic approach, it is not surprising that the number of the proteins identified is less than we have recently
Platelet Pathways Dys-Regulated by Diethyl Nitrosamine
research articles
Figure 5. Pathway modules showing the DEN-induced phase-to-phase differential regulation. The regulatory/signaling pathway modules uncovered by “clustering” the phase-specific differentially expressed proteins in: (A) complement and coagulation cascades for immune response, (B) apoptosis, (C) antigen processing and presentation pathway, (D) tissue wound repair, (E) cell proliferation. All proteins are color-coded according to their DEN-induced expression changes. The protein names in red or blue color indicate those up-regulated (red) or down-regulated (blue) proteins at a particular phase. Black suggests those unidentified or unchanged by our MS analysis. The protein abbreviations are given in Supplemental Table 4, S6 and Supplemental Table 5, Supporting Information.
reported by using multiple mass spectrometry-based approaches for the sole purpose of protein identification.39
Apparently over two-thirds of proteins quantified in each sample have up- or down-regulated compared to the control. Journal of Proteome Research • Vol. 9, No. 12, 2010 6215
research articles
Leng et al.
Figure 6. Subnetworks involved in multiple pathway modules showing DEN-induced phase-to-phase differential regulation. The subnetworks were assembled by those pathway components with their known or extended links to multiple pathways such as immune response, apoptosis, and tissue wound repair. The protein names in red or blue color indicate those up-regulated (red) or downregulated (blue) proteins at particular phase. Black suggests those unidentified or unchanged by our MS analysis. The protein abbreviations are given in Supplemental Table 4 and S6, Supporting Information.
Given the fact that thrombocytopenia is closely associated with chronic liver diseases, our interpretation of this observation is that the platelet proteome is highly sensitive to the pathological changes in the liver, implicating that the platelet proteome is a valid source for biomarker discovery. Because of our ability to simultaneously measure the regulatory dynamics of multiple proteins and the pathway modules they represent, we have obtained the systems view of how these pathways are functionally altered and interact with each other, and are coordinated in platelets during DEN perturbation. On a global view our results collectively indicated that in addition to their known roles, platelets can also function as the immune cells for the surveillance of tissue damage/transformation. By taking snapshots of the platelet proteome, as it dynamically changes with DEN-induced hepato-carcinogenesis (Supplemental Table 8, Supporting Information), we have shown that (1) at the early phases of DEN-induced acute inflammation in the liver, the pathway modules related to immune/inflammatory response, apoptosis, and tissue wound repair are overpresenting in the activated platelets. The pronounced or elevated outputs from these pathway modules could trigger the repair or apoptosis of the early damaged tissue. (2) In a progressive manner, major protein components in these path6216
Journal of Proteome Research • Vol. 9, No. 12, 2010
way modules/networks became differentially regulated and impaired with the development of HCC. The rat platelet proteome is closely correlated with the DEN-induced inflammatory and pathologic changes in liver tissue, which could mimic changes observed in human subjects with HCC. Due to the high dynamic nature of any proteome and the sensitivity of mass spectrometry measurements, it is anticipated that the expression changes of a large amount of proteins or in a significant portion of a proteome corresponding to any change in phenotype can be detected. Therefore, to distinguish potential biomarkers from those showing abundance changes caused by nonspecific stimuli, the threshold of the protein expression changes of physiological significance can only be determined on the basis of the analyses of a large number of clinically relevant biospecimens, which ensures sufficient statistical power in determining potential biomarkers. Here our platform is designed to quantitatively analyze the phase-tophase abundance changes of individual proteins and to categorize them in specific functional networks so that only those interconnected proteins showing the trend of the progressive changes are evaluated as possible HCC-specific markers. As a result, our comparative proteomic data set dissected the DENinduced dys-regulated machinery into multiple and intercon-
Platelet Pathways Dys-Regulated by Diethyl Nitrosamine
research articles
Figure 7. DEN-induced differentially regulated pathway modules associated with catabolism and metabolism. The proteins showing DEN-induced phase-specific expression changes were found to be involved in the regulatory networks associated with (A) metabolism and (B) catabolism. The protein names in red or blue color indicate those up-regulated (red) or down-regulated (blue) proteins at particular phase. Black indicates those unidentified or unchanged by our MS analysis. The protein abbreviations are given in Supplemental Table 7, Supporting Information.
nected protein targets that could be a reservoir of disease prognosis markers accessible noninvasively. Abbreviations: DEN, diethyl nitrosamine; HCC, hepatocellular carcinoma; MS, mass spectrometry; PF4, platelet factor 4; RANTES, regulated on activation normal T-cell expressed and secreted; TGFB1, transforming growth factor beta 1; TLR, Toll like receptor; TCEP, trichloroethyl phosphate; SCX, strong cation exchange; MMTS, methyl methanethiosulfonate; EF, error factor; H&E, hematoxylin and eosin; SD, standard deviation; PA, plasminogen activator; HCV, Hepatitis C virus; HBV, Hepatitis B virus; iTRAQ, isobaric tags for relative and absolute quantitation; ACO1, iron-responsive element-binding protein; ACO2, aconitase 2, mitochondrial; AHSG, alpha-2-hs-glycoprotein; ALDOAL1, hypothetical protein LOC299052; ALDOC, unnamed protein product; AMBP, alpha 1 microglobulin/ bikunin; APOA2, APOE and APOH, and APOM, apolipoprotein a-ii, e and h, and m; ALOX12, arachidonate 12-lipoxygenase; BAX, Bcl 2-associated X protein; BAT3, HLA-B-associated transcript 3 isoform 1; B2M, beta-2 microglobulin; CASP3, caspase 3; CALR, calreticulin; CANX, calnexin; CCT4 and CCT3 chaperonin subunit 4 and 3; CS, citrate synthase; CD44, CD44 antigen; CDC42, cell division cycle 42 homologue; CLU, clusterin; C9, C3, and C4A, complement component 9, 3, and 4a; CR2, complement receptor 2; CRP, Ba2-693; CRK, v-crk sarcoma virus CT10 oncogene homologue; DLAT, dihydrolipoamide S-acetyltransferase; DLST, dihydrolipoamide S-succinyltransferase; EGFR, epidermal growth factor receptor; EEF2, eukaryotic translation elongation factor 2; ENO1, eno1 protein; EEF1G, eukaryotic translation elongation factor 1 gamma; F2 and F5, coagulation factor 2 and 5; FGA, FGB, and FGG, fibrinogen, alpha, beta, and gamma polypeptide; FN1, fibronectin 1; F13A, coagulation factor XIII, A1 subunit; GAB2, GRB2-associated binding protein 2; GPX4, glutathione peroxidase 4; G6PDX, glucose-6-phosphate dehydrogenase; GPI, glucose phosphate isomerase; GSTP1, glutathione S-transferase,
pi; GP5 and GP9, glycoprotein 5 and 9; GRB2, growth factor receptor bound protein 2; HBB, beta-globin; HSPCA, heat shock protein 1, alpha; HSPA5, heat shock 70KD protein 5; HSPCB, heat shock 90KD protein 1, beta; HSPD1, 60 kDa heat shock protein, mitochondrial precursor; HSPE1, heat shock 10 kda protein 1; HCLS1, hematopoietic cell specific Lyn substrate 1; HRAS1, harvey rat sarcoma virus oncogene 1; IDH2, isocitrate dehydrogenase 2; IRS1, insulin receptor substrate 1; ILK, integrin linked kinase; ITGB3, integrin beta 3; INSR, insulin receptor; LCP2, lymphocyte cytosolic protein 2; LDHA, lactate dehydrogenase A; LYN, yamaguchi sarcoma viral oncogene homologue; KNG1, kininogen 1; KNG2, T-kininogen II precursor; MDH1 & MDH2, malate dehydrogenase 1 and 2; ME1, NADP-dependent malic enzyme; MTHFD1, methylenetetrahydrofolate dehydrogenase, methenyltetrahydrofolate cyclohydrolase, formyltetrahydrofolate synthase; MUG1, murinoglobulin 1 homologue; MCF2, MCF.2 cell line derived transforming sequence; NOGO, reticulon 4; PARK, park7 protein; PEPD, peptidase D; PFKP, phosphofructokinase, platelet; PGK1, phosphoglycerate kinase 1; PKM2, similar to pyruvate kinase isozyme M2; PRDX2, peroxiredoxin 2; PDCD6IP, programmed cell death 6 interacting protein; PIK3R2, phosphoinositide-3kinase, regulatory subunit 2; PLG, plasminogen; PSMA1 and PSMA4, proteasome (prosome, macropain) subunit, alpha type 1 and 4; PSMB1, PSMB 3, PSMB4, PSMB8, and PSMB10, proteasome (prosome, macropain) subunit, beta type 1, 3, 4, 8, and 10; PSMC3, PSMC4, and PSMC6, proteasome 26S ATPase subunit 3, 4, and 6; PSME1, protease 28 subunit, alpha; PDIA3, protein disulfide isomerase associated 3; RAC1, Ras-related C3 botulinum toxin substrate 1; RHOA, aplysia ras-related homologue A2; RT1-A1, RT1 class 1B, locus AW2; RAPB1, RAS related protein 1b; SERPINA1, serine proteinase inhibitor clade A, member 1; SERPINB2, serine protease inhibitor, clade b, member 2; SERPINC1, serine proteinase inhibitor, clade c, member 1; SERPIND1, serine proteinase inhibitor clade d, member 1; SERPINF2, Journal of Proteome Research • Vol. 9, No. 12, 2010 6217
research articles serine proteinase inhibitor, clade f, member 2; SELP, P-selectin; SH3KBP1, sh3-domain kinase binding protein 1; SHC1, SHC transforming protein 1; SOD1 and SOD2, superoxide dismutase 1 and 2; SPIN2C, serine protease inhibitor; SPIN2B, serine protease inhibitor; SRC, rous sarcoma oncogene; STK4, serine/threonine kinase 4; TAPBP, tap binding protein; TGFBR2, transforming growth factor, beta receptor 2; TGFA, transforming growth factor, alpha; TIMP3, tissue inhibitor of metalloproteinase 3; TF, transferring; TKT, transketolase; TPI1, triosephosphate isomerase 1; TALDO1, transaldolase 1; vWF, von willebrand factor; VDAC1, voltage-dependent anion channel 1; WAS, Wiskott-Aldrich syndrome homologue.
Acknowledgment. This work was supported by grants from Shanghai Science and Technology Development Program (Grants 03DZ14024 and 07ZR14010) and the 863 High Technology Foundation of China (Grant 2006AA02A310). This work was also supported by US NIH 1R01AI064806-01A2, and U.S. Department of Energy, the Office of Science (BER), Grant No. DE-FG02-07ER64422, and by University of North Carolina Cancer Research Fund. Supporting Information Available: Ten supplemental tables containing all identified platelet proteins and their quantifications between noninduced normal rats and DENinduced HCC rats as well as 3 figures. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Bosch, F. X.; Josepa, R.; Mireia, D.; Ramon, C. Primary liver cancer: Worldwide incidence and trends. Gastroenterology 2004, 127 (5), S5–S16. (2) Hanash, S. M.; Pitteri, S. J.; Faca, V. M. Mining the plasma proteome for cancer biomarkers. Nature 2008, 452 (7187), 571– 579. (3) Harrison, P. Platelet function analysis. Blood Rev. 2005, 19 (2), 111– 123. (4) Winkler, W.; Zellner, M.; Diestinger, M.; Babeluk, R.; Marchetti, M.; Goll, A.; Zehetmayer, S.; Bauer, P.; Rappold, E.; Miller, I.; Roth, E.; Allmaier, G.; Oehler, R. Biological Variation of the Platelet Proteome in the Elderly Population and Its Implication for Biomarker Research. Mol. Cell. Proteomics 2008, 7 (1), 193–203. (5) Afdhal, N.; McHutchison, J.; Brown, R.; Jacobson, I.; Manns, M.; Poordad, F.; Weksler, B.; Esteban, R. Thrombocytopenia associated with chronic liver disease. J. Hepatol. 2008, 48 (6), 1000–1007. (6) Lesurtel, M.; Graf, R.; Aleil, B.; Walther, D. J.; Tian, Y.; Jochum, W.; Gachet, C.; Bader, M.; Clavien, P.-A. Platelet-Derived Serotonin Mediates Liver Regeneration. Science 2006, 312 (5770), 104–107. ¨ zgen, U ¨ .; O ¨ zerol, E.; Aminci, M. Relationship between activation (7) O and apoptosis in platelets. Turkish J. Hematol. 2007, 24 (4), 171– 176. (8) von Hundelshausen, P.; Weber, C. Platelets as Immune Cells: Bridging Inflammation and Cardiovascular Disease. Circ. Res. 2007, 100 (1), 27–40. (9) Cervi, D.; Yip, T.-T.; Bhattacharya, N.; Podust, V. N.; Peterson, J.; Abou-Slaybi, A.; Naumov, G. N.; Bender, E.; Almog, N.; Italiano, J. E., Jr.; Folkman, J.; Klement, G. L. Platelet-associated PF-4 as a biomarker of early tumor growth. Blood 2008, 111 (3), 1201–1207. (10) Clark, S. R.; Ma, A. C.; Tavener, S. A.; McDonald, B.; Goodarzi, Z.; Kelly, M. M.; Patel, K. D.; Chakrabarti, S.; McAvoy, E.; Sinclair, G. D.; Keys, E. M.; Allen-Vercoe, E.; DeVinney, R.; Doig, C. J.; Green, F. H. Y.; Kubes, P. Platelet TLR4 activates neutrophil extracellular traps to ensnare bacteria in septic blood. Nat. Med. 2007, 13 (4), 463–469. (11) Lindemann, S.; Tolley, N. D.; Dixon, D. A.; McIntyre, T. M.; Prescott, S. M.; Zimmerman, G. A.; Weyrich, A. S. Activated Platelets Mediate Inflammatory Signaling by Regulated Interleukin 1b Synthesis. J. Cell Biol. 2001, 154 (3), 485–490. (12) Boing, A.; Hau, C.; Sturk, A.; Nieuwland, R. Platelet microparticles contain active caspase 3. Platelets 2008, 19 (2), 96–103. (13) Lee, J.-S.; Chu, I.-S.; Mikaelyan, A.; Calvisi, D. F.; Heo, J.; Reddy, J. K.; Thorgeirsson, S. S. Application of comparative functional genomics to identify best-fit mouse models to study human cancer. Nat. Genet. 2004, 36 (12), 1306–1311.
6218
Journal of Proteome Research • Vol. 9, No. 12, 2010
Leng et al. (14) Marcus, K.; Moebius, J.; Meyer, H. Differential analysis of phosphorylated proteins in resting and thrombin-stimulated human platelets. Anal. Bioanal. Chem. 2003, 376 (7), 973–993. (15) Shilov, I. V.; Seymour, S. L.; Patel, A. A.; Loboda, A.; Tang, W. H.; Keating, S. P.; Hunter, C. L.; Nuwaysir, L. M.; Schaeffer, D. A. The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra. Mol. Cell. Proteomics 2007, 6 (9), 1638–1655. (16) Huang, D. W.; Sherman, B. T.; Tan, Q.; Kir, J.; Liu, D.; Bryant, D.; Guo, Y.; Stephens, R.; Baseler, M. W.; Lane, H. C.; Lempicki, R. A. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, 35, W169–175. (17) Yang, F.; Zheng, S.; Jiang, T. Establishment of improved primary liver Carinoma model on rats. Natl. Med. J. China 2004, 83 (23), 2018–9. (18) Spooncer, E.; Brouard, N.; Nilsson, S. K.; Williams, B.; Liu, M. C.; Unwin, R. D.; Blinco, D.; Jaworska, E.; Simmons, P. J.; Whetton, A. D. Developmental Fate Determination and Marker Discovery in Hematopoietic Stem Cell Biology Using Proteomic Fingerprinting. Mol. Cell. Proteomics 2008, 7 (3), 573–581. (19) Ho, B.; Bendeck, M. P. Integrin linked kinase (ILK) expression and function in vascular smooth muscle cells. Cell Adh. Migr. 2009, 3 (2), 174–176. (20) Machida, K.; Matsuda, S.; Yamaki, K.; Senga, T.; Thant, A. A.; Kurata, H.; Miyazaki, K.; Hayashi, K.; Okuda, T.; Kitamura, T.; Hayakawa, T.; Hamaguchi, M. v-Src suppresses SHPS-1 expression via the Ras-MAP kinase pathway to promote the oncogenic growth of cells. Oncogene 2000, 19 (13), 1710–1718. (21) Bond, M.; Murphy, G.; Bennett, M. R.; Newby, A. C.; Baker, A. H. Tissue Inhibitor of Metalloproteinase-3 Induces a Fas-associated Death Domain-dependent Type II Apoptotic Pathway. J. Biol. Chem. 2002, 277 (16), 13787–13795. (22) Chen, L.; Wang, Y.; Ma, D.; Chen, Y. Short interfering RNA against the PDCD5 attenuates cell apoptosis and caspase-3 activity induced by Bax overexpression. Apoptosis 2006, 11 (1), 101–111. (23) Drew, A. F.; Liu, H.; Davidson, J. M.; Daugherty, C. C.; Degen, J. L. Wound-healing defects in mice lacking fibrinogen. Blood 2001, 97 (12), 3691–3698. (24) Schuster, V.; Hu ¨gle, B.; Tefs, K. Plasminogen deficiency. J. Thrombosis Haemostasis 2007, 5 (12), 2315–2322. (25) Ivaskevicius, V.; Seitz, R.; Kohler, H. P.; Schroeder, V.; Muszbek, L.; Ariens, R. A. S.; Seifried, E.; Oldenburg, J. International Registry on Factor XIII Deficiency: A basis formed mostly on European data. Thrombosis Haemostasis 2007, 97 (6), 914–921. (26) James, D. I.; Parone, P. A.; Mattenberger, Y.; Martinou, J.-C. hFis1, a Novel Component of the Mammalian Mitochondrial Fission Machinery. J. Biol. Chem. 2003, 278 (38), 36373–36379. (27) Del Re, D. P.; Miyamoto, S.; Brown, J. H. RhoA/Rho Kinase Upregulate Bax to Activate a Mitochondrial Death Pathway and Induce Cardiomyocyte Apoptosis. J. Biol. Chem. 2007, 282 (11), 8069–8078. (28) Reddy, R. K.; Lu, J.; Lee, A. S. The Endoplasmic Reticulum Chaperone Glycoprotein GRP94 with Ca2+-binding and Antiapoptotic Properties Is a Novel Proteolytic Target of Calpain during Etoposide-induced Apoptosis. J. Biol. Chem. 1999, 274 (40), 28476– 28483. (29) Watari, A.; Yutsudo, M. Multi-functional gene ASY/Nogo/RTN-X/ RTN4: Apoptosis, tumor suppression, and inhibition of neuronal regeneration. Apoptosis 2003, 8 (1), 5–9. (30) Sasaki, T.; Gan, E. C.; Wakeham, A.; Kornbluth, S.; Mak, T. W.; Okada, H. HLA-B-associated transcript 3 (Bat3)/Scythe is essential for p300-mediated acetylation of p53. Genes Dev. 2007, 21 (7), 848– 861. (31) Narita, T.; Nishimura, T.; Yoshizaki, K.; Taniyama, T. CIN85 associates with TNF receptor 1 via Src and modulates TNF-[alpha]induced apoptosis. Exp. Cell Res. 2005, 304 (1), 256–264. (32) Waterhouse, N.; Pinkoski, M. Calreticulin: Raising awareness of apoptosis. Apoptosis 2007, 12 (4), 631–634. (33) Mahul-Mellier, A.-L.; Hemming, F. J.; Blot, B.; Fraboulet, S.; Sadoul, R. Alix, Making a Link between Apoptosis-Linked Gene-2, the Endosomal Sorting Complexes Required for Transport, and Neuronal Death In Vivo. J. Neurosci. 2006, 26 (2), 542–549. (34) Koukourakis, M. I.; Kontomanolis, E.; Giatromanolaki, A.; Sivridis, E.; Liberis, V. Serum and Tissue LDH Levels in Patients with Breast/ Gynaecological Cancer and Benign Diseases. Gynecol. Obstet. Invest. 2008, 67 (3), 162–168.
research articles
Platelet Pathways Dys-Regulated by Diethyl Nitrosamine (35) Skawran, B.; Steinemann, D.; Weigmann, A.; Flemming, P.; Becker, T.; Flik, J.; Kreipe, H.; Schlegelberger, B.; Wilkens, L. Gene expression profiling in hepatocellular carcinoma: upregulation of genes in amplified chromosome regions. Mod. Pathol. 2008, 21 (5), 505–516. (36) Takashima, M.; Kuramitsu, Y.; Yokoyama, Y.; Iizuka, N.; Fujimoto, M.; Nishisaka, T.; Okita, K.; Oka, M.; Nakamura, K. Overexpression of alpha enolase in hepatitis C virus-related hepatocellular carcinoma: Association with tumor progression as determined by proteomic analysis. Proteomics 2005, 5 (6), 1686–1692. (37) Hamaguchi, T.; Iizuka, N.; Tsunedomi, R.; Hamamoto, Y.; Miyamoto, T.; Iida, M.; Tokuhisa, Y.; Sakamoto, K.; Takashima, M.; Tamesa, T.; Oka, M. Glycolysis module activated by hypoxia-
inducible factor 1alpha is related to the aggressive phenotype of hepatocellular carcinoma. Int. J. Oncol. 2008, 33 (4), 725–731. (38) Maeda, S.; Kamata, H.; Luo, J.-L.; Leffert, H.; Karin, M. IKKbeta Couples Hepatocyte Death to Cytokine-Driven Compensatory Proliferation that Promotes Chemical Hepatocarcinogenesis. Cell 2005, 121 (7), 977–990. (39) Yu, Y.; Leng, T.; Yun, D.; Liu, N.; Yao, J.; Dai, Y.; Yang, P.; Chen, X. Global analysis of the rat and human platelet proteome - the molecular blueprint for illustrating multi-functional platelets and cross-species function evolution. Proteomics 2010, 10 (13), 2444–2457.
PR100679T
Journal of Proteome Research • Vol. 9, No. 12, 2010 6219
