Comprehensive Peptidome Analysis of Mouse Livers by Size

ated by the proteolytic enzymes in the body,20 which can be considered as the .... software Armone written in-house to delete keratins and the redunda...
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Comprehensive Peptidome Analysis of Mouse Livers by Size Exclusion Chromatography Prefractionation and NanoLC-MS/MS Identification Lianghai Hu, Xin Li, Xinning Jiang, Houjiang Zhou, Xiaogang Jiang, Liang Kong, Mingliang Ye, and Hanfa Zou* National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China Received September 11, 2006

Peptidome analysis has received increasing attention in recent years. Cancer diagnosis by serum peptidome has also been reported by peptides’ profiling for discovery of peptide biomarkers. Tissue, which may have a higher biomarker concentration than blood, has not been investigated extensively by means of peptidome analysis. Here, a method for the peptidome analysis of mouse liver was developed by the combination of size exclusion chromatography (SEC) prefractionation with nanoliquid chromatography-tamdem mass spectrometry (nanoLC-MS/MS) analysis. The extracted peptides from mouse liver were separated according to their molecular weight using a size exclusion column. MALDI-TOF MS was used to characterize the molecular weight distribution of the peptides in fractions eluted from the SEC column. The low molecular weight (LMW) (MW < 3000 Da) peptides in the collected fractions were directly analyzed by LC-MS/MS which resulted in the identification of 1181 unique peptides (from 371 proteins). The high molecular weight (HMW) (MW > 3000 Da) peptides in the early two fractions from the SEC column were first digested with trypsin, and the resulted digests were then analyzed by LC-MS/MS, which led to the identification of 123 and 127 progenitor proteins of the HMW peptides in fractions 1 and 2, respectively. Analysis of the peptides’ cleavage sites showed that the peptides are cleaved in regulation, which may reflect the protease activity and distribution in body, and also represent the biological state of the tissue and provide a fresh source for biomarker discovery. Keywords: peptidomics • proteomics • mouse liver • size exclusion chromatography • mass spectrometry

Introduction Proteomic research has progressed quickly in recent years, and the intensive separation and detection technology for largescale identification of proteins with trace sample has become feasible.1,2 However, only a few proteins are validated as disease biomarkers because of the low abundance of the potential biomarkers and the complexity of biological samples.3 The low molecular weight (LMW) fraction of proteome, which was usually considered as biological trash,4 has attracted increasing attention recently.5-7 The peptidome has been proposed for comprehensive study of peptides or LMW proteins expressed by a cell, tissue, and organs of an organism.8,9 The peptidome analysis mainly focused on the quantitative and qualitative analysis of peptides, which can be divided into two classes: (I) the endogenous peptides that exert vital functions in biological processes such as hormones, cytokines, growth factors,9-12 and MHC class I peptides13; (II) the degraded fragments of proteins which reflect the proteolytic enzyme * Address correspondence to Prof. Dr. Hanfa Zou, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China. E-mail, hanfazou@ dicp.ac.cn; tel., +86-411-84379610; fax, +86-411-84379620. 10.1021/pr060469e CCC: $37.00

 2007 American Chemical Society

species and biological state of individual.14,15 The endogenous peptides play crucial roles in the respiratory, cardiovascular, endocrine, inflammatory, and nervous systems.11,16 The discovery of novel neuropeptides has been extensively studied, and some databases have been established for the endogenous peptides.12,17-19 The degraded fragments of proteins are generated by the proteolytic enzymes in the body,20 which can be considered as the metabolic products of proteins. Circulating protein fragments generated in the body fluid or tissues may reflect the biological events and provide a rich bank for diagnostic biomarkers.21 The profiling of peptides generated in serum is also being developed for cancer diagnosis.22-25 Villanueva et al.22 used MALDI-TOF MS-based peptides profiling to distinguish prostate, bladder, and breast cancer patients from healthy persons successfully. The peptide concentration in tissues should be higher than that in the blood, and thus, screening of biomarkers in the tissues may be another way to speed up biomarker discovery.26 However, there is minimal information reported thus far on peptidome analysis.27,28 The liver is one of the most important organs in the body and plays crucial functions. It is considered as the main “chemical factory” and “energy plant” for the body.29 Journal of Proteome Research 2007, 6, 801-808

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research articles Liquid chromatography-tandem mass spectrometry (LCMS/MS) is mostly used for identification of peptides due to its high detection sensitivity and high throughput. Andren et al.11 used nanoLC-ESI-Q-TOF MS to identify the endogenous neuropeptides in brain tissue to discover novel bioactive peptides. Hancock et al.30 used LTQ-FTMS to analyze the peptidome of human serum filtrate, and over 300 unique peptides were identified. However, few peptides over 3000 Da were directly identified with available approaches. Here, a method for comprehensive analysis of mouse liver peptidome was developed. First, the peptides in mouse liver were harvested by ultrafiltration with a 10 kDa molecular weight cutoff (MWCO). Then, the filtrate sample was prefractionated using size exclusion chromatography, and totally, 18 fractions were then collected every 30 s for each fraction. The collected fractions were analyzed by MALDI-TOF MS to show their molecular weight distribution. All the fractions were directly analyzed by nanoLC-MS/MS without trypsin digests, and totally, 1181 peptides (from 371 proteins) were identified. The high molecular weight (HMW) peptides (over 3000 Da) in early eluted fractions 1 and 2, from which only a few peptides can be identified, were then digested with trypsin and analyzed by nanoLC-MS/MS, and 123 and 127 progenitor proteins of the HMW peptides were identified, respectively.

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Figure 1. Overlapping of the identified peptides in three different mouse livers.

Experimental Section Chemicals. Adult female C57 mice were obtained from Dalian Medical University (Dalian, China). Formic acid, acetic acid, trifluoroacetic acid (TFA), and TPCK-treated trypsin were purchursed from Sigma (St. Louis, MO). Dithiothreitol (DTT) and iodoacetamide (IAA) were all purchased from Bio-Rad (Hercules, CA). Water used in all procedures was prepared using a Milli-Q system (Milford, MA). Peptide Extraction. Mice were sacrificed, and the livers were promptly removed and washed with extract buffer (0.25% acetic acid) to remove the blood. Then, the livers were minced with scissors and homogenized with extract buffer of 0.25% acetic acid to a concentration of 0.2 g tissue/mL.10-12,16 The suspension was then sonicated for 90 s at 450 W and centrifuged at 25 000g for 1 h. Then, the supernatant was transferred to a centrifugal filter device (Amicon Ultra-15, Millipore, Milford, MA) with a nominal molecular mass limit of 10 000 Da. After centrifugation at 5000g for 30 min at 4 °C, the filtrate was collected and lyophilized. Then, the lyophilized peptides’ sample was redissolved in 0.1% formic acid at 6 g tissue/mL and stored at -20 °C for following experiments. Size Exclusion Chromatography (SEC) Prefractionation of the Peptide Sample. Size exclusion column was TSK SuperSW2000 (4 µm, 125 Å, 300 mm × 4.6 mm i.d.) from TOSOH company (Tokyo, Japan). The mobile phase was delivered by an LC-10ADvp pump (Shimadu, Kyoto, Japan); the detector was SPD-M10Avp UV-vis detector (Shimadzu, Kyoto, Japan), and the chromatographic data were collected with WDL-95 workstation software (National Chromatographic R&A Center, Dalian, China). Chromatographic conditions are as follows: mobile phase, 45% acetonitrile (ACN) in 0.1% TFA buffer (isocratic elution); flow rate, 0.35 mL/min; UV detection wavelength, 214 nm. Digestion of the HMW Peptides. Fractions 1 and 2 collected from the SEC column were lyophilized to dryness and redissolved in 150 µL of a solution of 50 mM ammonium bicarbonate by addition of 1 µL of 1 M DTT. The mixture was incubated at 37 °C for over 2 h, and then1 µL of 1 M IAA was added and 802

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Figure 2. Chromatograms for the size exclusion chromatographic separation of (a) standard sample containing cytochrome c, insulin, and insulin chain B and (b) the extracted peptides from mouse liver.

incubated for an additional 30 min at room temperature in darkness. Then, 1 µL of 1 µg/µL trypsin was added and incubated at 37 °C overnight. The digested products were lyophilized to dryness and redissolved in 5 µL of 0.1% formic acid for LC-MS/MS analysis. MALDI-TOF MS. MALDI-TOF MS was performed on the Bruker AutoflexTM instrument (Bruker Co., Bremen, Germany). The instrument was equipped with a nitrogen laser (λ ) 337 nm), and its available accelerating potential is in the range of

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Figure 3. MALDI-TOF MS spectra for characterization of the collected fractions 1-8 from size exclusion chromatographic column.

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research articles +20/-20 kV. The MALDI uses a ground-steel sample target. All mass spectra were obtained in the positive ion detection mode. Three microliters of DHB containing 0.1% trifluoroacetic acid was added to 1 µL of peptide sample, and then a 1 µL aliquot of the resulting solution was deposited onto the target for analysis. NanoLC-MS/MS. A Finnigan surveyor MS pump (ThermoFinnigan, San Jose, CA) was used to deliver the mobile phase. The pump flow rate was split using a micro-splitter valve to achieve a column flow rate of about 200 nL/min. For the capillary separation column, one end of the fused-silica capillary (75 µm i.d. × 120 mm length) was manually pulled to a fine point of ∼5 µm with a flame torch. The column was inhouse-packed with C18 AQ particles (5 µm, 120 Å) from Michrom BioResources (Auburn, CA) using a pneumatic pump. The µRPLC column was directly coupled to a LTQ linear ion trap mass spectrometer from ThermoFinnigan (San Jose, CA) with a nanospray source. The LTQ instrument was operated at positive ion detection mode. A voltage of 1.8 kV was applied to the cross. The temperature to heat capillary was set at 170 °C. Normalized collision energy was 35.0. The number of ions stored in the ion trap was regulated by the automatic gain control. Voltages across the capillary and the quadrupole lenses were tuned by an automated procedure to maximize the signal for the ion of interest. The mass spectrometer was set at one full MS scan followed by 10 MS/MS scans on the 10 most intense ions from the MS spectrum. The mobile phase consisted of A, 0.1% formic acid in water, and B, 0.1% formic acid in acetonitrile. The gradient elution program was set as follows: 98% A-90% A (0-3 min), 90% A-65% A (3-36 min), 65% A-20% A (36-38 min), 20% A-20% A (38-48 min), 20%-98% (48-51 min), 98% A-98%A (51-60 min). Data Processing and Analysis. Protein/peptide identifications were searched against database (downloaded from ftp://ftp.ebi.ac.uk/pub/databases/IPI/old/MOUSE/ipi.MOUSE.v3.08.fasta.gz) using the SEQUEST algorithm from Thermo Electron (San Jose, CA). Search parameters used were as follows: no enzyme, no static modification were set; variable modification was set for oxidation on methionine (when searching the data of trypsin digests sample, fixed modification carbamidomethylation was set on cysteine and the enzyme was set partially tryptic), the mass type of peptide is set at monoisotopic. Output results were combined together using software Armone written in-house to delete keratins and the redundant data. The search results were first filtrated by setting lowest Xcorr as 1.9, 2.2, and 3.75 corresponding to 1+, 2+, and 3+ charge states, respectively. A minimum delta correlation (∆Cn) of 0.2 was required for an identification to be considered positive. The false-positive rate (FPR) of the peptide identification was determined by performing Sequest searching against a composite database that includes both regular and reverse protein sequences. FPRs were calculated using the following equation: FPR ) 2n(rev)/[n(rev) + n(forw)], where n(forw) and n(rev) are the number of peptides identified in proteins with forward (normal) and reversed sequence, respectively.37,38

Results and Discussion Direct Analysis of the Peptide Extract by NanoLC-MS/MS. The extracted peptides were directly analyzed by nanoLC-MS/ MS three times. About 110 peptides can be identified in a single run, and totally, 215 peptides (from 87 proteins) were identified in all three runs. Commonly, when we dealt with the search 804

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Figure 4. Number of the peptides and proteins identified in each fraction.

Figure 5. Distribution of the 495 proteins identified by direct analysis and after trypsin digestion of HMW fractions.

results with trypsin digests, ∆Cn threshold was set at 0.1. However, to get a high confidence for identification of peptides without digestion, we set the ∆Cn threshold at 0.2. Searching against the reversed database was performed to evaluate the identification reliability. The false-positive rate was 5.33% with ∆Cn threshold at 0.2. More than 99% of the identified peptides had the MW below 3000 Da, and MW of most peptides was at around 1600 Da (see Supporting Information). To decrease the degradation of the proteins, we performed all the procedures below 4 °C, and the extraction buffer was at about pH 3.0. Most proteases were inactive in these conditions. To further evaluate the repeatability of the extraction process, livers from three mice were extracted and analyzed by nanoLCMS/MS. The recovery of the peptides was shown in Figure 1. About 38% of the identified peptides overlapped in the extract of three mice, which indicated the extraction process was repeatable. SEC Prefractionation and MALDI-TOF MS Characterization. Until recently, analysis of the LMW proteome was achieved either by trypsin digests of the ultrafiltrate fractions or by direct mass spectrometric detection. Tirumalai et al.31 used centrifugal filters with 30 000 Da molecular weight cutoff (MWCO) to isolate a large number of small proteins as well as the peptides in serum. Then, the obtained ultrafiltrate was digested with trypsin and analyzed by LC-MS/MS resulting in identification of 340 proteins. Hancock et al.30 used centrifugal filter with 10 000 Da molecular weight cutoff (MWCO) to isolate the peptides in serum and then directly analyzed them by LTQ-FTMS analysis resulting in identification of more

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Figure 6. Selected peptide ladder sequence and the locations of the 10 ladders identified in carbamoyl-phosphate synthetase 1.

than 300 unique peptides. However, few peptides were identified even with high accuracy LTQ-FTMS by direct analysis of the HMW peptides because it is beyond the ability of MS/MS for identification of peptides with a molecular weight over 3000 Da. There have been no reports to separate the HMW peptides from the LMW peptides for peptidome analysis. In our protocol for peptidome analysis of mouse livers, the HMW peptides were separated from the LMW peptides by SEC column. To evaluate whether the SEC can achieve a satisfactory separation of peptides below 10 000 Da, a standard sample containing cytochrome c (12 327 Da), insulin (5734 Da), and insulin chain B (3496 Da) was separated on the SEC column, and the obtained chromatogram is shown in Figure 2a. It can be seen that the three standards were well-separated with high column efficiency. This indicated that SEC can provide enough resolution for fractionation of peptide sample based on the molecular sizes of peptides. The extracted peptides from mouse livers were separated with the SEC column as shown in Figure 2b. The fractions were collected from the SEC column every 30 s automatically and characterized by MALDI-TOF MS analysis. There was no peptide eluted before 6.5 min according to UV detection and off-line MALDI-TOF MS analysis. Totally, 18 fractions were collected from 6.5 to 15.5 min. Figure 3 showed the MALDI-TOF MS spectra for the first 8 fractions eluted from the SEC column. The samples of the collected fractions after fraction 8 were not easily dried on the MALDI

target probably due to the presence of some nonpeptide substances in these fractions and, thus, were not analyzed by MALDI-TOF MS. It can be clearly seen in the MS spectra in Figure 3 that the first two eluted fractions contain the peptides with a molecular weight of over 3000 Da and the other fractions contain peptides mainly between 1000 and 3000 Da. NanoLC-MS/MS Analysis of the Collected Fractions. Each of the collected fractions was analyzed by nanoLC-MS/MS system. The number of identified peptides and corresponding proteins from each fraction were shown in Figure 4. From fractions 1-18, we can totally identify 1181 peptides (from 371 proteins) (see the Supporting Information). To the best of our knowledge, this is the largest number of peptides identified for peptidome analysis. Not only the total number of peptides identified was increased greatly by introducing of SEC for prefractionation of the extracted samples, but also the number of peptide identifications in a single injection and data credibility were greatly improved. For example, in fraction 5, we identified 543 peptides which were 5 times of those identified by direct analysis of sample without prefractionation. To check whether the search results were reliable, the MS data for fractions 3 and 5 were searched against the reversed database. The false-positive rates are 3.06% and 1.43% with ∆Cn threshold at 0.2, respectively, which is much lower than that obtained by direct analysis of the extracted sample without prefractionation (5.33%). This is probably because the quality of MS/MS Journal of Proteome Research • Vol. 6, No. 2, 2007 805

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Figure 7. Distribution of the four cleavage sites in the identified peptides.

spectra was improved due to the elimination of the low-mass contaminates in the sample after SEC fractionation. The molecular weight distribution of the identified peptides with SEC prefractionation is also much more symmetrical than that obtained by direct analysis of peptide extract without prefractionation (see Supporting Information). MALDI-TOF MS showed that most peptides in fractions 1 and 2 have molecular weight over 3000 Da, which is beyond the identification ability of LC-MS/MS. Only 8 and 17 peptides were identified by direct LC-MS/MS analysis of fractions 1 and 2. Then, these two fractions were digested with trypsin, and the digested products were analyzed by LC-MS/MS again. Correspondingly, 123 and 127 proteins in fractions 1 and 2 were identified after digestion. Many of the identified proteins after digestion in those two fractions were the same as the proteins identified by the peptides with direct LC-MS/MS analysis, which indicated that most of the HMW and LMW peptides were generated by the same proteins but cleaved by different proteases or in different degree. However, there were also some proteins that can only be identified from the peptides of HMW fractions digestion. For example, serum albumin was only identified from the peptides in fraction 1 (based on 11 peptides) and fraction 2 (based on 1 peptide), while no peptide from serum albumin was identified in all fractions by direct LCMS/MS analysis. LC-MS/MS analysis of the tryptic digest of HMW peptides allows the identification of their progenitor proteins, but their full sequences cannot be determined. New approaches to determine the sequence of peptides with MW > 3000 Da need to be developed in the future. We combined the identified proteins of direct analysis and trypsin digestion, and totally, 495 proteins were obtained. Figure 5 showed the distribution of the 495 proteins. About 36% (71) proteins identified from the HMW peptides over806

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lapped with those identified from the LMW peptides. The proteins were characterized according to their molecular weight (MW), isoelectric point (pI), and grand average hydrophobicity (GRAVY) values. The proteins were also categorized based upon their biological process, cellular component, and molecular function (see the Supporting Information). These classifications showed that the precursor proteins of the peptides have a broad range of distribution, exist in different subcellular locations, have a wide scale of physicochemical properties, and take part in a large number of biological processes and carry out crucial biological functions. Peptides Cleavage Pattern. With further analysis of the identified peptides, we can observe that many of the peptides from one protein come from the same segment, which was also observed in the serum peptidome analysis.30 The generation of peptide ladder was not random. The enzymes in tissue cleaved the protein according to their rules. For example, no peptide from serum albumin, one of the abundant proteins in mouse liver, was detected by direct analysis of the extracted peptides. However, the peptides from serum albumin can be detected in the digests of HMW fractions. On the contrary, the protein of carbamoyl-phosphate synthetase 1 generated the highest number of peptides (131 peptides) in our case, which meant it was most easily cleaved by the endogenous enzymes. However, the 131 peptides corresponded to only 10 segments in the protein sequence. Figure 6 showed the sequences of peptides identified from one of these segments. Hancock et al.30 referred to it as peptide ladder because the peptides arrange like a ladder. The locations of the 10 peptide ladders identified in carbamoyl-phosphate synthetase 1 were marked in Figure 6, and the top 5 proteins and their corresponding peptide ladders were listed in the Supporting Information.

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We further analyzed the cleavage site distribution, and the obtained results for the four cleavage sites were shown in Figure 7. We can see that leucine (L) was the most hit cleavage site for C-terminal amino acid of the preceding peptide, while lysine (K) dominated the cleavage site of C-terminal amino acid of the identified peptide. It was postulated that the HMW peptides were produced by endoproteolytic cleavages of their progenitor proteins, then other enzymes such as exoproteases of aminopeptidase and carboxypeptidase further cleaved the HMW peptides to produce the LMW peptides.30,33,34 The cleavage sites’ distribution of peptidome in mouse livers was different from that of serum peptidome.30 This can be interpreted to mean that the proteases varied greatly in different tissue and body fluid. To see whether the cleavage pattern was consistent between different individuals, we examined the distribution of the cleavage sites of the identified peptides from three mouse livers. Their cleavage sites’ distribution almost keeps consistent among different individuals. This may be caused by the activities of proteases in the body which keep their stabilization during the extract process. Proteases often represent a host response to the physiological states and can play a critical role in mediating the communication between a biological event and its microenvironment.35 For example, the proteases regulate in different disease states,36 which will result in the difference of degradation pattern, and also the degradation fragments can be the biomarkers of cancer.37 A cleavage fragment of inter-R-trypsin inhibitor heavy chain H4 has been validated as biomarker of ovarian cancer for the regulation of protease.37 This not only reflects the physiological state of an individual, but also can reveal the pathological mechanism and therapeutic purpose to some level.

Conclusion We have developed an approach for comprehensive analysis of the peptidome of mouse livers. By SEC prefractionation of the peptide extraction, the HMW peptides (eluted in fractions 1 and 2) were separated from the LMW peptides, and this allowed further study of the HMW peptides. After digestion of the HMW peptides with trypsin, 123 and 127 progenitor proteins were identified from fractions 1 and 2, respectively, and approaches for getting the full sequence of the HMW peptides still need to be developed. Altogether, 1181 LMW peptides were identified by direct analysis of the collected fractions without digestion . Compared with other published literatures, this is a full-scale characterization of peptidome on both LMW and HMW peptides. The cleavage sites’ distribution of the identified peptides further reflects the protease activity and distribution in the body. Proteases are highly regulated and reactive to the signaling pathways operational during tumor progression,35 thus, the peptide cleavage patterns expressed by proteases in the body can be useful for cancer diagnosis and therapeutic targets. Abbreviations: HMW, high molecular weight; LMW, low molecular weight; SEC, size exclusion chromatography; LTQFTMS, linear trap quadrupole-Fourier transform mass spectrometry; LC-MS/MS, liquid chromatography-mass spectrometry/mass spectrometry; CID, collision-induced dissociation; MALDI, matrix-assisted laser desorption ionization; TOF, timeof-flight; MWCO, molecular weight cutoff.

Acknowledgment. This work was supported by National Natural Sciences Foundation of China (No. 20327002), the

China State Key Basic Research Program Grant (2005CB522701), the Knowledge Innovation Program of CAS (KSCX2-YW-N-035), and the Knowledge Innovation Program of Dalian Institute of Chemical Physics, Chinese Academy of Sciences (to H.Z.).

Supporting Information Available: Characterization of the identified peptides and proteins; peptides and their corresponding proteins identified in fractions 1-18; top 5 proteins and their peptide ladders. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Washburn, M. P.; Wolters, D.; Yates, J. R. Nat. Biotechnol. 2001, 19, 242-247. (2) Aebersold, R.; Mann, M. Nature 2003, 422, 198-207. (3) Ludwig, J. A.; Weinstein, J. N. Nat. Rev. Cancer 2005, 5, 845-856. (4) Novak, K. Nat. Rev. Cancer 2006, 6, 92-92. (5) Soloviev, M.; Finch, P. Proteomics 2006, 6, 744-747. (6) Adermann, K.; John, H.; Standker, L.; Forssmann, W. G. Curr. Opin. Biotechnol. 2004, 15, 599-606. (7) Geho, D. H.; Liotta, L. A.; Petricoin, E. F.; Zhao, W. D.; Araujo, R. P. Curr. Opin. Chem. Biol. 2006, 10, 50-55. (8) Schulz-Knappe, P.; Zucht, H. D.; Heine, C.; Jurgens, M.; Hess, R.; Schrader, M. Comb. Chem. High Throughput Screening 2001, 4, 207-217. (9) Schrader, M.; Schulz-Knappe, P. Trends Biotechnol. 2001, 19, S55-S60. (10) Baggerman, G.; Verleyen, P.; Clynen, E.; Huybrechts, J.; De Loof, A.; Schoofs, L. J. Chromatogr., B 2004, 803, 3-16. (11) Svensson, M.; Skold, K.; Svenningsson, P.; Andren, P. E. J. Proteome Res. 2003, 2, 213-219. (12) Falth, M.; Skold, K.; Norrman, M.; Svensson, M.; Fenyo, D.; Andren, P. E. Mol. Cell. Proteomics 2006, 5, 998-1005. (13) Yewdell, J. W. Science 2003, 301, 1334-1335. (14) Petricoin, E. F.; Ardekani, A. M.; Hitt, B. A.; Levine, P. J.; Fusaro, V. A.; Steinberg, S. M.; Mills, G. B.; Simone, C.; Fishman, D. A.; Kohn, E. C.; Liotta, L. A. Lancet 2002, 359, 572-577. (15) Villanueva, J.; Philip, J.; Entenberg, D.; Chaparro, C. A.; Tanwar, M. K.; Holland, E. C.; Tempst, P. Anal. Chem. 2004, 76, 15601570. (16) Skold, K.; Svensson, M.; Kaplan, A.; Bjorkesten, L.; Astrom, J.; Andren, P. E. Proteomics 2002, 2, 447-454. (17) Clynen, E.; Baggerman, G.; Veelaert, D.; Cerstiaens, A.; Van der Horst, D.; Harthoorn, L.; Derua, R.; Waelkens, E.; De Loof, A.; Schoofs, L. Eur. J. Biochem. 2001, 268, 1929-1939. (18) Fricker, L. D.; Lim, J. Y.; Pan, H.; Che, F. Y. Mass Spectrom. Rev. 2006, 25, 327-344. (19) Minamino, N.; Tanaka, J.; Kuwahara, H.; Kihara, T.; Satomi, Y.; Matsubae, M.; Takao, T. J. Chromatogr., B 2003, 792, 33-48. (20) Fricker, L. D. AAPS J. 2005, 7, E449-E455. (21) Liotta, L. A.; Petricoin, E. F. J. Clin. Invest. 2006, 116, 26-30. (22) Villanueva, J.; Shaffer, D. R.; Philip, J.; Chaparro, C. A.; ErdjumentBromage, H.; Olshen, A. B.; Fleisher, M.; Lilja, H.; Brogi, E.; Boyd, J.; Sanchez-Carbayo, M.; Holland, E. C.; Cordon-Cardo, C.; Scher, H. I.; Tempst, P. J. Clin. Invest. 2006, 116, 271-284. (23) Villanueva, J.; Martorella, A. J.; Lawlor, K.; Philip, J.; Fleisher, M.; Robbins, R. J.; Tempst, P. Mol. Cell. Proteomics 2006, 5, 18401852. (24) Mian, S.; Ugurel, S.; Parkinson, E.; Schlenzka, I.; Dryden, I.; Lancashire, L.; Ball, G.; Creaser, C.; Rees, R.; Schadendorf, D. J. Clin. Oncol. 2005, 23, 5088-5093. (25) Robbins, R. J.; Villanueva, J.; Tempst, P. J. Clin. Oncol. 2005, 23, 4835-4837. (26) Cottingham, K. J. Proteome Res. 2006, 5, 1047-1048. (27) Traub, F.; Jost, M.; Hess, R.; Schorn, K.; Menzel, C.; Budde, P.; Schulz-Knappek, P.; Lamping, N.; Pich, A.; Kreipe, H.; Tammen, H. Lab. Invest. 2006, 86, 246-253. (28) Schulte, L.; Tammen, H.; Selle, H.; Schulz-Knappe, P. Expert Rev. Mol. Diagn. 2005, 5, 145-157. (29) He, F. C. Mol. Cell. Proteomics 2005, 4, 1841-1848. (30) Zheng, X. Y.; Baker, H.; Hancock, W. S. J. Chromatogr., A 2006, 1120, 173-184. (31) Tirumalai, R. S.; Chan, K. C.; Prieto, D. A.; Issaq, H. J.; Conrads, T. P.; Veenstra, T. D. Mol. Cell. Proteomics 2003, 2, 1096-1103. (32) Koomen, J. M.; Li, D. H.; Xiao, L. C.; Liu, T. C.; Coombes, K. R.; Abbruzzese, J.; Kobayashi, R. J. Proteome Res. 2005, 4, 972-981. (33) Diamandis, E. P. J. Proteome Res. 2006, 5, 2079-2082.

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research articles (34) Matrisian, L. M.; Sledge, G. W.; Mohla, S. Cancer Res. 2003, 63, 6105-6109. (35) Boire, A.; Covic, L.; Agarwal, A.; Jacques, S.; Sherifl, S.; Kuliopulos, A. Cell 2005, 120, 303-313. (36) Zhang, Z.; Bast, R. C.; Yu, Y. H.; Li, J. N.; Sokoll, L. J.; Rai, A. J.; Rosenzweig, J. M.; Cameron, B.; Wang, Y. Y.; Meng, X. Y.; Berchuck, A.; van Haaften-Day, C.; Hacker, N. F.; de Bruijn, H.

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Hu et al. W. A.; van der Zee, A. G. J.; Jacobs, I. J.; Fung, E. T.; Chan, D. W. Cancer Res. 2004, 64, 5882-5890. (37) Peng, J. M.; Elias, J. E.; Thoreen, C. C.; Licklider, L. J.; Gygi, S. P. J. Proteome Res. 2003, 2, 43-50. (38) Xie, H.; Bandhakavi, S.; Griffin, T. J. Anal. Chem. 2005, 77, 3198-3207.

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