Efficient Microscale Basic Reverse Phase Peptide Fractionation for Global and Targeted Proteomics.

Analysis of small biological samples would benefit from an efficient microscale fractionation strategy that minimizes sample handling, transfer steps, and accompanying losses. Here we describe a microscale basic reverse phase liquid chromatographic (bRPLC) fractionation method that offers high reproducibility and efficiency for peptide mixtures from small (5-20 µg) samples. We applied our platform to detect differentially expressed proteins from lung tumor cell lines that are sensitive (11-18) and resistant (11-18R) to the tyrosine kinase inhibitor erlotinib. Label-free analyses of 5-20 µg samples yielded identifications of approximately 3,200 to 4,000 proteins with coefficients of variation of 1.9-8.9% in replicate analyses. iTRAQ analyses produced similar protein inventories. Label-free and iTRAQ analyses displayed high concordance in identifications of proteins differentially expressed in 11-18 and 11-18R cells. Micro-bRPLC fractionation of cell proteomes increased sensitivity by an average of 4.5-fold in targeted quantitation using parallel reaction monitoring for three representative receptor tyrosine kinases (EGFR, PDGFRA, and BMX), which are present at low abundance in 11-18 and 11-18R cells. These data illustrate the broad utility of micro-bRPLC fractionation for global and targeted proteomic analyses. Data are available through Proteome eXchange Accession PXD003604.

Quantitative Profiling of Protein Tyrosine Kinases in Human Cancer Cell Lines by Multiplexed Parallel Reaction Monitoring Assays.

Protein tyrosine kinases (PTKs) play key roles in cellular signal transduction, cell cycle regulation, cell division, and cell differentiation. Dysregulation of PTK-activated pathways, often by receptor overexpression, gene amplification, or genetic mutation, is a causal factor underlying numerous cancers. In this study, we have developed a parallel reaction monitoring-based assay for quantitative profiling of 83 PTKs. The assay detects 308 proteotypic peptides from 54 receptor tyrosine kinases and 29 nonreceptor tyrosine kinases in a single run. Quantitative comparisons were based on the labeled reference peptide method. We implemented the assay in four cell models: 1) a comparison of proliferating versus epidermal growth factor-stimulated A431 cells, 2) a comparison of SW480Null (mutant APC) and SW480APC (APC restored) colon tumor cell lines, and 3) a comparison of 10 colorectal cancer cell lines with different genomic abnormalities, and 4) lung cancer cell lines with either susceptibility (11-18) or acquired resistance (11-18R) to the epidermal growth factor receptor tyrosine kinase inhibitor erlotinib. We observed distinct PTK expression changes that were induced by stimuli, genomic features or drug resistance, which were consistent with previous reports. However, most of the measured expression differences were novel observations. For example, acquired resistance to erlotinib in the 11-18 cell model was associated not only with previously reported up-regulation of MET, but also with up-regulation of FLK2 and down-regulation of LYN and PTK7. Immunoblot analyses and shotgun proteomics data were highly consistent with parallel reaction monitoring data. Multiplexed parallel reaction monitoring assays provide a targeted, systems-level profiling approach to evaluate cancer-related proteotypes and adaptations. Data are available through Proteome eXchange Accession PXD002706.

Integrated Proteomic and Genomic Analysis of Gastric Cancer Patient Tissues.

V-erb-b2 erythroblastic leukemia viral oncogene homologue 2, known as ERBB2, is an important oncogene in the development of certain cancers. It can form a heterodimer with other epidermal growth factor receptor family members and activate kinase-mediated downstream signaling pathways. ERBB2 gene is located on chromosome 17 and is amplified in a subset of cancers, such as breast, gastric, and colon cancer. Of particular interest to the Chromosome-Centric Human Proteome Project (C-HPP) initiative is the amplification mechanism that typically results in overexpression of a set of genes adjacent to ERBB2, which provides evidence of a linkage between gene location and expression. In this report we studied patient samples from ERBB2-positive together with adjacent control nontumor tissues. In addition, non-ERBB2-expressing patient samples were selected as comparison to study the effect of expression of this oncogene. We detected 196 proteins in ERBB2-positive patient tumor samples that had minimal overlap (29 proteins) with the non-ERBB2 tumor samples. Interaction and pathway analysis identified extracellular signal regulated kinase (ERK) cascade and actin polymerization and actinmyosin assembly contraction as pathways of importance in ERBB2+ and ERBB2- gastric cancer samples, respectively. The raw data files are deposited at ProteomeXchange (identifier: PXD002674) as well as GPMDB.

Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project.

Approximately 2.9 billion long base-pair human reference genome sequences are known to encode some 20 000 representative proteins. However, 3000 proteins, that is, ~15% of all proteins, have no or very weak proteomic evidence and are still missing. Missing proteins may be present in rare samples in very low abundance or be only temporarily expressed, causing problems in their detection and protein profiling. In particular, some technical limitations cause missing proteins to remain unassigned. For example, current mass spectrometry techniques have high limits and error rates for the detection of complex biological samples. An insufficient proteome coverage in a reference sequence database and spectral library also raises major issues. Thus, the development of a better strategy that results in greater sensitivity and accuracy in the search for missing proteins is necessary. To this end, we used a new strategy, which combines a reference spectral library search and a simulated spectral library search, to identify missing proteins. We built the human iRefSPL, which contains the original human reference spectral library and additional peptide sequence-spectrum match entries from other species. We also constructed the human simSPL, which contains the simulated spectra of 173 907 human tryptic peptides determined by MassAnalyzer (version 2.3.1). To prove the enhanced analytical performance of the combination of the human iRefSPL and simSPL methods for the identification of missing proteins, we attempted to reanalyze the placental tissue data set (PXD000754). The data from each experiment were analyzed using PeptideProphet, and the results were combined using iProphet. For the quality control, we applied the class-specific false-discovery rate filtering method. All of the results were filtered at a false-discovery rate of <1% at the peptide and protein levels. The quality-controlled results were then cross-checked with the neXtProt DB (2014-09-19 release). The two spectral libraries, iRefSPL and simSPL, were designed to ensure no overlap of the proteome coverage. They were shown to be complementary to spectral library searching and significantly increased the number of matches. From this trial, 12 new missing proteins were identified that passed the following criterion: at least 2 peptides of 7 or more amino acids in length or one of 9 or more amino acids in length with one or more unique sequences. Thus, the iRefSPL and simSPL combination can be used to help identify peptides that have not been detected by conventional sequence database searches with improved sensitivity and a low error rate.

Comparative Study for Efficacy and Safety of Adenoidectomy according to the Surgical Method: A Prospective Multicenter Study.

BACKGROUND/OBJECTIVE: There have been several operative techniques for adenoidectomy and their efficacy and morbidity are different according to the technique. This prospective multicenter study was aimed to compare the efficacy and morbidity of coblation adenoidectomy (CA) with those of power-assisted adenoidectomy. STUDY DESIGN: Prospective multi-institutional study. METHODS: Children who underwent CA, power-assisted adenoidectomy with cauterization (PAA+C) or without cauterization (PAA-C) due to adenoid hypertrophy were enrolled from 13 hospitals between July 2013 and June 2014. Mean operation time, degree of intraoperative bleeding and postoperative bleeding rate were evaluated. RESULTS: A total of 388 children (mean age ± standard deviation = 6.6 ± 2.5 years; 245 males and 143 females) were included. According to the adenoidectomy technique, the children were classified into 3 groups: (1) CA (n = 116); (2) PAA+C (n = 153); and (3) PAA-C (n = 119). Significant differences were not found in age and sex among three groups. In the CA group, mean operation time was significantly shorter (P < 0.001) and degree of intraoperative bleeding was significantly less (P < 0.001) compared to PAA+C or PAA-C group. Delayed postoperative bleeding rate of PAA-C group was significantly higher than that of CA or PAA+C group (P = 0.016). CONCLUSIONS: This prospective multicenter study showed that CA was superior to PAA in terms of mean operation time and degree of intraoperative bleeding.

Distinct Protein Expression Profiles of Solid-Pseudopapillary Neoplasms of the Pancreas.

Solid-pseudopapillary neoplasm (SPN) is an uncommon pancreatic tumor with mutation in CTNNB1 and distinct clinical and pathological features. We compared the proteomic profiles of SPN to mRNA expression. Pooled SPNs and pooled non-neoplastic pancreatic tissues were examined with high-resolution mass spectrometry. We identified 329 (150 up-regulated and 179 down-regulated) differentially expressed proteins in SPN. We identified 191 proteins (58.1% of the 329 dysregulated proteins) with the same expression tendencies in SPN based on mRNA data. Many overexpressed proteins were related to signaling pathways known to be activated in SPNs. We found that several proteins involved in Wnt signaling, including DKK4 and ß-catenin, and proteins that bind ß-catenin, such as FUS and NONO, were up-regulated in SPNs. Molecules involved in glycolysis, including PKM2, ENO2, and HK1, were overexpressed in accordance to their mRNA levels. In summary, SPN showed (1) distinct protein expression changes that correlated with mRNA expression, (2) overexpression of Wnt signaling proteins and proteins that bind directly to ß-catenin, and (3) overexpression of proteins involved in metabolism. These findings may help develop early diagnostic biomarkers and molecular targets.

Abundance-ratio-based semiquantitative analysis of site-specific N-linked glycopeptides present in the plasma of hepatocellular carcinoma patients.

Aberrant structures of site-specific N-linked glycans are closely associated with the tumorigenesis of hepatocellular carcinoma (HCC), one of the most common fatal cancers worldwide. Vitronectin (VTN) is considered a candidate glycobiomarker of HCC. In this study, we describe a reliable and simple quantification strategy based on abundance ratios of site-specific N-linked glycopeptides of VTN to screen for potential biomarkers. A total of 14 unique N-linked glycans corresponding to 27 unique N-linked glycopeptides were characterized at three N-linked sites (Asn-86, -169, and -242) present in VTN. These glycans could be good candidate markers for HCC. Among these glycans, the abundance ratio of two representative glycoforms (fucosyl vs non-fucosyl) was significantly increased in HCC plasma relative to normal plasma. This strategy was also successfully applied to another potential HCC biomarker, haptoglobin. Furthermore, we demonstrate that our approach employing tandem mass tag (TMT) and target N-linked glycopeptides of VTN is a useful tool for quantifying specific glycans in HCC plasma relative to normal plasma. Our strategy represents a simple and potentially useful screening platform for the discovery of cancer-specific glycobiomarkers.

Proteogenomic analysis of human chromosome 9-encoded genes from human samples and lung cancer tissues.

The Chromosome-centric Human Proteome Project (C-HPP) was recently initiated as an international collaborative effort. Our team adopted chromosome 9 (Chr 9) and performed a bioinformatics and proteogenomic analysis to catalog Chr 9-encoded proteins from normal tissues, lung cancer cell lines, and lung cancer tissues. Approximately 74.7% of the Chr 9 genes of the human genome were identified, which included approximately 28% of missing proteins (46 of 162) on Chr 9 compared with the list of missing proteins from the neXtProt Master Table (2013-09). In addition, we performed a comparative proteomics analysis between normal lung and lung cancer tissues. On the basis of the data analysis, 15 proteins from Chr 9 were detected only in lung cancer tissues. Finally, we conducted a proteogenomic analysis to discover Chr 9-residing single nucleotide polymorphisms (SNP) and mutations described in the COSMIC cancer mutation database. We identified 21 SNPs and four mutations containing peptides on Chr 9 from normal human cells/tissues and lung cancer cell lines, respectively. In summary, this study provides valuable information of the human proteome for the scientific community as part of C-HPP. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000603.

Enhanced delivery of T cells to tumor after chemotherapy using membrane-anchored, apoptosis-targeted peptide.

Chemotherapy-induced apoptosis of tumor cells enhances the antigen presentation and sensitizes tumor cells to T cell-mediated cytotoxicity. Here we harnessed the apoptosis of tumor cells as a homing signal for the delivery of T cells to tumor. Jurkat T cells were anchored with ApoPep-1, an apoptosis-targeted peptide ligand, using the biocompatible anchor for membrane (BAM), an oleyl acid derivative. The ApoPep-1-BAM conjugate was efficiently anchored to cell membrane, while little anchoring was obtained with ApoPep-1 alone. The retention period of the ApoPep-1-BAM conjugate on cell membrane was approximately 80 and 40 min in the absence and presence of serum, respectively. ApoPep-1 was resistant to degradation in serum until 2h. The apoptosis-targeted T cells that were anchored with the ApoPep-1-BAM preferentially bound to apoptotic tumor cells over living cells. When intravenously injected into tumor-bearing mice, the number of apoptosis-targeted T cells and in vivo fluorescence signals by the homing of the cells to doxorubicin-treated tumor were higher than those of untargeted T cells. Accumulation of apoptosis-targeted T cells at other organs such as liver was not detected. These results suggest that the chemotherapy-induced apoptosis and subsequent enhancement of T cell delivery to tumor by the membrane anchoring of the apoptosis-targeted peptide could be a novel strategy for cancer immunotherapy.

Enhanced peptide quantification using spectral count clustering and cluster abundance.

BACKGROUND: Quantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, such as spectral counting and spectra feature analysis have been extensively investigated in a wide variety of proteomic studies. The cornerstone of both methods is peptide identification based on a proteomic database search and subsequent estimation of peptide retention time. However, they often suffer from restrictive database search and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification methods based on the spectral library search algorithms such as SEQUEST or SpectraST have been found to provide neither the best match nor high-scored matches. Lastly, these methods are limited in the sense that target peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries.To overcome these limitations, we propose a novel method, namely Quantification method based on Finding the Identical Spectral set for a Homogenous peptide (Q-FISH) to estimate the peptide's abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Intuitively, our Q-FISH method compares all possible pairs of experimental spectra in order to identify both known and novel proteins, significantly enhancing identification accuracy by grouping replicated spectra from the same peptide targets. RESULTS: We applied Q-FISH to Nano-LC-MS/MS data obtained from human hepatocellular carcinoma (HCC) and normal liver tissue samples to identify differentially expressed peptides between the normal and disease samples. For a total of 44,318 spectra obtained through MS/MS analysis, Q-FISH yielded 14,747 clusters. Among these, 5,777 clusters were identified only in the HCC sample, 6,648 clusters only in the normal tissue sample, and 2,323 clusters both in the HCC and normal tissue samples. While it will be interesting to investigate peptide clusters only found from one sample, further examined spectral clusters identified both in the HCC and normal samples since our goal is to identify and assess differentially expressed peptides quantitatively. The next step was to perform a beta-binomial test to isolate differentially expressed peptides between the HCC and normal tissue samples. This test resulted in 84 peptides with significantly differential spectral counts between the HCC and normal tissue samples. We independently identified 50 and 95 peptides by SEQUEST, of which 24 and 56 peptides, respectively, were found to be known biomarkers for the human liver cancer. Comparing Q-FISH and SEQUEST results, we found 22 of the differentially expressed 84 peptides by Q-FISH were also identified by SEQUEST. Remarkably, of these 22 peptides discovered both by Q-FISH and SEQUEST, 13 peptides are known for human liver cancer and the remaining 9 peptides are known to be associated with other cancers. CONCLUSIONS: We proposed a novel statistical method, Q-FISH, for accurately identifying protein species and simultaneously quantifying the expression levels of identified peptides from mass spectrometry data. Q-FISH analysis on human HCC and liver tissue samples identified many protein biomarkers that are highly relevant to HCC. Q-FISH can be a useful tool both for peptide identification and quantification on mass spectrometry data analysis. It may also prove to be more effective in discovering novel protein biomarkers than SEQUEST and other standard methods.

Targeted mass spectrometric approach for biomarker discovery and validation with nonglycosylated tryptic peptides from N-linked glycoproteins in human plasma.

A simple mass spectrometric approach for the discovery and validation of biomarkers in human plasma was developed by targeting nonglycosylated tryptic peptides adjacent to glycosylation sites in an N-linked glycoprotein, one of the most important biomarkers for early detection, prognoses, and disease therapies. The discovery and validation of novel biomarkers requires complex sample pretreatment steps, such as depletion of highly abundant proteins, enrichment of desired proteins, or the development of new antibodies. The current study exploited the steric hindrance of glycan units in N-linked glycoproteins, which significantly affects the efficiency of proteolytic digestion if an enzymatically active amino acid is adjacent to the N-linked glycosylation site. Proteolytic digestion then results in quantitatively different peptide products in accordance with the degree of glycosylation. The effect of glycan steric hindrance on tryptic digestion was first demonstrated using alpha-1-acid glycoprotein (AGP) as a model compound versus deglycosylated alpha-1-acid glycoprotein. Second, nonglycosylated tryptic peptide biomarkers, which generally show much higher sensitivity in mass spectrometric analyses than their glycosylated counterparts, were quantified in human hepatocellular carcinoma plasma using a label-free method with no need for N-linked glycoprotein enrichment. Finally, the method was validated using a multiple reaction monitoring analysis, demonstrating that the newly discovered nonglycosylated tryptic peptide targets were present at different levels in normal and hepatocellular carcinoma plasmas. The area under the receiver operating characteristic curve generated through analyses of nonglycosylated tryptic peptide from vitronectin precursor protein was 0.978, the highest observed in a group of patients with hepatocellular carcinoma. This work provides a targeted means of discovering and validating nonglycosylated tryptic peptides as biomarkers in human plasma, without the need for complex enrichment processes or expensive antibody preparations.

A new versatile peptide-based size exclusion chromatography platform for global profiling and quantitation of candidate biomarkers in hepatocellular carcinoma specimens.

Disease biomarkers are predicted to be in low abundance; thus, the most crucial step of biomarker discovery is the efficient fractionation of clinical samples into protein sets that define disease stages and/or predict disease development. For this purpose, we developed a new platform that uses peptide-based size exclusion chromatography (pep-SEC) to quantify disease biomarker candidates. This new platform has many advantages over previously described biomarker profiling platforms, including short run time, high resolution, and good reproducibility, which make it suitable for large-scale analysis. We combined this platform with isotope labeling and label-free methods to identify and quantitate differentially expressed proteins in hepatocellular carcinoma (HCC) tissues. When we combined pep-SEC with a gas phase fractionation method, which broadens precursor ion selection, the protein coverage was significantly increased, which is critical for the global profiling of HCC specimens. Furthermore, pep-SEC-LC-MS/MS analysis enhanced the detection of low-abundance proteins (e.g. insulin receptor substrate 2 and carboxylesterase 1) and glycopeptides in HCC plasma. Thus, our pep-SEC platform is an efficient and versatile pre-fractionation system for the large-scale profiling and quantitation of candidate biomarkers in complex disease proteomes.

Contribution of sams-1 and pmt-1 to lipid homoeostasis in adult Caenorhabditis elegans.

Accumulation of lipids inside the cell is primarily caused by disorders of lipid metabolism. S-adenosylmethionine synthetase (SAMS) produces SAM, an important methyl donor in various phospholipid methyltransferase reactions catalysed by phosphoethanolamine N-methyltransferase (PMT-1). A gel-based, quantitative proteomic analysis of the RNA interference (RNAi)-mediated inactivation of the pod-2 gene, which encodes acetyl-CoA carboxylase, showed a substantial down-regulation of SAMS-1. Consequently, RNAi of either sams-1 or pmt-1 caused a significant increase in lipid droplet size in the intestine of Caenorhabditis elegans. Lipid droplets exhibited increased triacylglycerol (TG) and decreased phosphatidylcholine (PC) levels, suggesting a reciprocal relationship between TG and PC regulation. These lipid-associated phenotypes were rescued by choline feeding. Among the five fat metabolism-related genes examined, two genes were highly induced by inactivation of sams-1 or pmt-1: pod-2 and stearoyl-CoA desaturase (fat-7). Thus, both SAMS-1 and PMT-1 were shown to contribute to the homoeostasis of TG and PC levels in C. elegans, which would provide an important survival strategy under harsh environmental conditions.

Anti-Inflammatory activity of Angelica keiskei through suppression of mitogen-activated protein kinases and nuclear factor-kappaB activation pathways.

Angelica keiskei has been shown to exhibit antitumor, antioxidant, and antidiabetic activities, and the fresh leaves and dry powder are used for health food. In spite of several beneficial effects, however, the molecular mechanism or mechanisms behind anti-inflammatory activities of A. keiskei remain unclear. Thus, we investigated the effects of A. keiskei on the activities of inducible nitric oxide (NO) synthase (iNOS) and cyclooxygenase-2 (COX-2) in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. We found that the n-hexane fraction of A. keiskei (HAK) significantly inhibited LPS-induced NO and prostaglandin E(2) production and tumor necrosis factor-alpha secretion. HAK also inhibited the expression of LPS-induced iNOS and COX-2 proteins and their mRNA levels. Furthermore, we hypothesize that anti-inflammatory effects by HAK can be linked to interference with the signaling pathway of mitogen-activated protein kinases (MAPKs) and the activation pathway of nuclear factor kappaB (NF-kappaB). HAK suppressed LPS-induced c-Jun NH(2)-terminal kinase, p38, and p44/p42 MAPK activation. We also found that the cell-based assay system showed that HAK suppressed LPS-induced NF-kappaB activity in transfectant RAW 264.7 cells. In addition, the electrophoretic mobility shift assay showed the same result as in the cell-based assay system. Our data suggest that the anti-inflammatory effect of HAK is mediated through down-modulation of iNOS and COX-2 gene products by blocking the signaling pathways of MAPKs and NF-kappaB.

Simple method for quantitative analysis of N-linked glycoproteins in hepatocellular carcinoma specimens.

Changes in N-linked glycan structures are related to the initiation and progression of hepatocellular carcinoma (HCC), one of the most common fatal cancers worldwide. In this study, we describe a simple and an efficient strategy for the selective identification and quantitation of N-linked glycoproteins that does not require extensive enrichment steps prior to MS/MS analysis. With this approach, N-linked glycoprotein differences between the plasma of healthy and HCC patients were selectively quantified after iTRAQ labeling. We identified a total of 14 N-linked glycopeptides with higher expression in HCC patient plasma than in healthy controls (>or=1.5 fold). Additionally, we characterized alterations in the glycan structures of vitronectin (Asn-169, 242) and antithrombin III (Asn-225) that were identified in HCC patient plasma. The intact glycopeptides with native glycan structures were also elevated in HCC tumor tissue. Taken together, these data support the utility of our approach for high throughput global profiling and quantification of the N-linked glycopeptides to identify disease biomarkers.

Human plasma carboxylesterase 1, a novel serologic biomarker candidate for hepatocellular carcinoma.

To identify and characterize a serologic glycoprotein biomarker for hepatocellular carcinoma (HCC), multi-lectin affinity chromatography was used to isolate intracellular N-linked glycoprotein fractions from five paired non-tumor and tumor tissues. From the series of 2-D DIGE targeted differentially expressed N-linked glycoproteins, we identified human liver carboxylesterase 1 (hCE1), which was remarkably down-regulated in tumor tissues, a finding confirmed by Western blot, a quantitative real-time RT-PCR, and immunohistochemical staining of non-tumor and tumor tissues from total 58 HCC patients. To investigate whether hCE1 is also present in human plasma, we employed a magnetic bead-based immunoprecipitation followed by nano-LC-MS/MS analysis, and we found for the first time that hCE1 is present in human plasma as opposed to that in liver tissues. That is, from normalization of hCE1 signal by the immunoprecipitation and Western blot analysis, hCE1 levels were increased in plasma specimens from HCC patients than in plasma from other disease patient groups (e.g. liver cirrhosis, chronic hepatitis, cholangiocarcinoma, stomach cancer, and pancreatic cancer). From the receiver operating characteristic analysis in HCC, both sensitivity and specificity were shown to be greater than 70.0 and 85.0%, respectively. Thus, the high-resolution proteomic approach demonstrates that hCE1 is a good candidate for further validation as a serologic glycoprotein biomarker for HCC.

BiomarkerDigger: a versatile disease proteome database and analysis platform for the identification of plasma cancer biomarkers.

We have developed a proteome database (DB), BiomarkerDigger (http://biomarkerdigger.org) that automates data analysis, searching, and metadata-gathering function. The metadata-gathering function searches proteome DBs for protein-protein interaction, Gene Ontology, protein domain, Online Mendelian Inheritance in Man, and tissue expression profile information and integrates it into protein data sets that are accessed through a search function in BiomarkerDigger. This DB also facilitates cross-proteome comparisons by classifying proteins based on their annotation. BiomarkerDigger highlights relationships between a given protein in a proteomic data set and any known biomarkers or biomarker candidates. The newly developed BiomarkerDigger system is useful for multi-level synthesis, comparison, and analyses of data sets obtained from currently available web sources. We demonstrate the application of this resource to the identification of a serological biomarker for hepatocellular carcinoma by comparison of plasma and tissue proteomic data sets from healthy volunteers and cancer patients.

Quantitative analysis of phosphopeptides in search of the disease biomarker from the hepatocellular carcinoma specimen.

Reversible phosphorylation of proteins is the most common PTM in cell-signaling pathways. Despite this, high-throughput methods for the systematic detection, identification, and quantification of phosphorylated peptides have yet to be developed. In this paper, we describe the establishment of an efficient online titaniuim dioxide (TiO2)-based 3-D LC (strong cationic exchange/TiO2/C18)-MS(3)-linear ion trap system, which provides fully automatic and highly efficient identification of phosphorylation sites in complex peptide mixtures. Using this system, low-abundance phosphopeptides were isolated from cell lines, plasma, and tissue of healthy and hepatocellular carcinoma (HCC) patients. Furthermore, the phosphorylation sites were identified and the differences in phosphorylation levels between healthy and HCC patient specimens were quantified by labeling the phosphopeptides with isotopic analogs of amino acids (stable isotope labeling with amino acids in cell culture for HepG2 cells) or water (H(2) (18)O for tissues and plasma). Two examples of potential HCC phospho-biomarkers including plectin-1(phopho-Ser-4253) and alpha-HS-glycoprotein (phospho-Ser 138 and 312) were identified by this analysis. Our results suggest that this comprehensive TiO2-based online-3-D LC-MS(3)-linear ion trap system with high-throughput potential will be useful for the global profiling and quantification of the phosphoproteome and the identification of disease biomarkers.

Application of a peptide-based PF2D platform for quantitative proteomics in disease biomarker discovery.

A peptide-based 2-D liquid phase fractionation (PF2D) system was used in a quantitative proteomic analysis of hepatocellular carcinoma. 2-D liquid maps of peptide specimens showed better resolution than those of proteins, leading to the identification of differentially expressed proteins. Peptide-based PF2D gave well-matched theoretical and experimental pI values and was proven to be a very efficient and versatile analytical tool for both large-scale profiling and quantification of phosphoproteins in disease biomarker discovery.

Protein profiling of human plasma samples by two-dimensional electrophoresis.

Human plasma is regarded the most complex and well-known clinical specimen that can be easily obtained; alterations in the levels of plasma proteins or their corresponding enzyme activities may reflect either a healthy or a diseased state. Given that there is no defined genomic information as to the intact protein components in plasma, protein profiling could be the first step toward its molecular characterization. Several problems exist in the analysis of plasma proteins, however. For example, the widest dynamic range of protein concentrations, the presence of high-abundance proteins, and post-translational modifications need to be considered before proteomic studies are undertaken. In particular, efficient depletion or pre-fractionation of high-abundance proteins is crucial for the identification of low-abundance proteins that may contain potential biomarkers. After the removal of high-abundance proteins, protein profiling can be initiated using two-dimensional electrophoresis (2DE), which has been widely used for displaying the differential proteome under specific physiological conditions. Here, we describe a typical 2DE procedure for plasma proteome under either a healthy or a diseased state (e.g., liver cancer) in which pre-fractionation and depletion are integral steps in the search for disease biomarkers.