Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Selective Identification of α-Galactosyl Epitopes in N-Glycoproteins Using Characteristic Fragment Ions from Higher-Energy Collisional Dissociation.

The α-galactosyl epitope is a terminal N-glycan moiety of glycoproteins found in mammals except in humans, and thus, it is recognized as an antigen that provokes an immunogenic response in humans. Accordingly, it is necessary to analyze the α-galactosyl structure in biopharmaceuticals or organ transplants. Due to an identical glycan composition and molecular mass between α-galactosyl N-glycans and hybrid/high-mannose-type N-glycans, it is challenging to characterize α-galactosyl epitopes in N-glycoproteins using mass spectrometry. Here, we describe a method to identify α-galactosyl N-glycopeptides in mice glycoproteins using liquid chromatography with tandem mass spectrometry with higher-energy collisional dissociation (HCD). The first measure was an absence of [YHM] ion peaks in the HCD spectra, which was exclusively observed in hybrid and/or high-mannose-type N-glycopeptides. The second complementary criterion was the ratio of an m/z 528.19 (Hex2HexNAc1) ion to m/z 366.14 (Hex1HexNAc1) ion (Im/z528/Im/z366). The measure of [Im/z528/Im/z366 > 0.3] enabled a clear-cut determination of α-galactosyl N-glycopeptides with high accuracy. In Ggta1 knockout mice, we could not find any α-galactosyl N-glycoproteins identified in WT mice plasma. Using this method, we could screen for α-galactosyl N-glycoproteins from mice spleen, lungs, and plasma samples in a highly sensitive and specific manner. Conclusively, we suggest that this method will provide a robust analytical tool for determination of α-galactosyl epitopes in pharmaceuticals and complex biological samples.

Computational classification of core and outer fucosylation of N-glycoproteins in human plasma using collision-induced dissociation in mass spectrometry.

RATIONALE: Glycoprotein fucosylation, one of the major posttranslational modifications, is known to be highly involved in proteins related to various cancers. Fucosylation occurs in the core and/or outer sites of N-glycopeptides. Elucidation of the fucosylation type of N-glycoproteins is therefore important. However, it has remained a challenge to classify the fucosylation types of N-glycopeptides using collision-induced dissociation (CID) tandem mass (MS/MS) spectra. METHODS: The relative intensities of the Y1 F, Y2 F, Y3 F, and Y4 F product ions in the CID-MS/MS spectra of the IgG N-glycopeptides were measured for core fucosylation. The Core Fucose Index (CFI) was then calculated by multiplication of the relative intensities with a weight factor from logistic regression to differentiate between the core and none fucosylation. From the relative intensities of the B2 F and B3 SF ions of the MS/MS spectra of the AGP N-glycopeptides for outer fucosylation, the Outer Fucose Index (OFI) was calculated to differentiate between the outer and none fucosylation. RESULTS: In order to classify core and/or outer fucosylation of N-glycoproteins, we defined the fucosylation score (F-score) by a sigmoidal equation using a combination of the CFI and the OFI. For application, we classified the fucosylation types of N-glycoproteins in human plasma with 99.7% accuracy from the F-score. Human plasma samples showed 54.4%, 33.3%, 10.3%, and 1.6% for none, core, outer, and dual fucosylated N-glycopeptides, respectively. Core fucosylation was abundant at mono- and bi-antennary N-glycopeptides. Outer fucosylation was abundant at tri- and tetra-antennary N-glycopeptides. In total, 113 N-glycopeptides of 29 glycoproteins from 3365 glycopeptide spectral matches (GPSMs) were classified for different types of fucosylation. CONCLUSIONS: We established an F-score to classify three different fucosylation types: core, outer, and dual types of N-glycopeptides. The fucosylation types of 20 new N-glycopeptides from 11 glycoproteins in human plasma were classified using the F-score. Therefore, the F-score can be useful for the automatic classification of different types of fucosylation in N-glycoproteins of biological fluids including plasma, serum, and urine.

BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds.

The Bioinformatics & Molecular Design Research Center Mass Spectral Library - Natural Products (BMDMS-NP) is a library containing the mass spectra of natural compounds, especially plant specialized metabolites. At present, the library contains the electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra of 2739 plant metabolites that are commercially available. The contents of the library were made comprehensive by incorporating data generated under various experimental conditions for compounds with diverse molecular structures. The structural diversity of the BMDMS-NP data was evaluated using molecular fingerprints, and it was sufficiently exhaustive enough to represent the structures of the natural products commercially available. The MS/MS spectra of each metabolite were obtained with different types/brands of ion traps (tandem-in-time) or combinations of mass analyzers (tandem-in-space) at multiple collision energies. All spectra were measured repeatedly in each environment because variations can occur in spectra, even under the same conditions. Moreover, the probability, separability of searching, and transferability of this spectral library were evaluated against those of MS/MS libraries, namely: NIST17 and MoNA.

Classification of Mucin-Type O-Glycopeptides Using Higher-Energy Collisional Dissociation in Mass Spectrometry.

Changes in mucin-type O-glycosylation of human proteins affect protein function, immune response, and cancer progression. Since O-glycoproteins are characterized by the microheterogeneity of diverse O-glycans with no conserved sequence and the macroheterogeneity of multiple glycosylation sites on serine and/or threonine in human proteins, the assessment of different mucin types, such as Tn-antigen, core 1, and core 2, and their extended core types in O-glycopeptides, is extremely challenging. Here, we present an O-GlycoProteome Analyzer (O-GPA) that automatically classifies mucin-type O-glycosylation using higher-energy collisional dissociation (HCD) in mass spectrometry. First, we estimated the number of GlcNAc residues using the intensity ratio of GlcNAc-specific fragment ions (HexNAc-CH6O3 and HexNAc-2H2O) over GalNAc-specific fragment ions (HexNAc-C2H6O3 and HexNAc-C2H4O2) in the HCD spectrum. Furthermore, we classified the different mucin types of O-glycopeptides from characteristic B2 (HexNAc2) or Y2α (PEP + HexNAc2), and Y2ß (PEP + HexNAcHex) fragment ions, along with the number of GlcNAc. Furthermore, O-GPA automatically determined single or multiple O-glycosylation, regardless of the mucin types. The mucin type of O-glycopeptides from human urine and plasma was confirmed with an overall accuracy of 96%. We found 97 core 1, 56 core 2, 13 extended core 1, and 12 extended core 2 glycopeptides from urine; and 22 core 1, 13 core 2, 7 extended core 1, 1 extended core 2, and 1 Tn-antigen from plasma. Our strategy can be used to successfully characterize specific mucin types of O-glycoproteins in human biological samples.

Author Correction: Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry.

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

Parallel reaction monitoring with multiplex immunoprecipitation of N-glycoproteins in human serum for detection of hepatocellular carcinoma.

The N-glycosylation of proteins is one of the most important post-translational modifications relevant to various biological functions. The identification and quantification of N-glycoproteins in liquid chromatography-mass spectrometry (LC-MS) is challenging because of their low analytical sensitivity and selectivity. This is due to their microheterogeneity and the difficulty of synthesizing N-glycopeptides as an internal standard. Parallel reaction monitoring (PRM) is widely used in targeted LC-MS. The key advantage of LC-PRM is that it can identify N-glycopeptides using tandem mass spectrometry (MS/MS) fragmentation, even without an internal standard. We investigated the feasibility of analyzing N-glycoproteins using multiplex immunoprecipitation to improve sensitivity and selectivity. We targeted N-glycoproteins [α-fetoprotein (AFP), vitronectin (VTN), and α-1-antichymotrypsin (AACT)] that are abnormally glycosylated in hepatocellular carcinoma (HCC). Their tryptic N-glycopeptides were selected to determine the percentages of fucosylated N-glycopeptides using Y ions, which include glycopeptide fragments with amino acid sequences. Finally, we confirmed that the area under the receiver operating characteristic curve (AUC = 0.944) for the combination of AFP and VTN increased more so than for a single glycopeptide (AUC = 0.889 for AFP and 0.792 for VTN) with respect to discriminating between HCC and cirrhosis serum. This study shows that an LC-PRM method using multiplex N-glycoproteins immunoprecipitated from serum could be applied to develop and verify cancer biomarkers. Graphical abstract.

Isomer separation of sialylated O- and N-linked glycopeptides using reversed-phase LC-MS/MS at high temperature.

Analyses of intact glycopeptides using mass spectrometry is challenging due to the numerous types of isomers of glycan moieties attached to the peptide backbone. Here, we demonstrate that high-temperature reversed-phase liquid chromatography (RPLC) can be used to separate isomeric O- and N-linked glycopeptides. In general, high column temperatures enhanced the resolution for separation of sialylated O- and N-linked glycopeptide isomers with decreased retention times. Using the high-temperature RPLC method, α2-6-linked sialylated N-glycopeptides were eluted first, followed by α2-3-linked isomers. However, highly sialylated N-glycopeptides containing hydrophobic amino acids exhibited increased retention times at high temperature. The separation of sialylated O- and N-glycopeptides with different glycan isoforms using a high-temperature RPLC method was demonstrated. This study indicates that reversed-phase chromatographic separation at high column temperatures is suitable for the separation of glycopeptide structural isomers.

Next Generation Proteomic Pipeline for Chromosome-Based Proteomic Research Using NeXtProt and GENCODE Databases.

Human Proteome Project aims to map all human proteins including missing proteins as well as proteoforms with post translational modifications, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). neXtProt and Ensemble databases are usually used to provide curated information on human coding genes. However, to find these proteoforms, we (Chr #11 team) first introduce a streamlined pipeline using customized and concatenated neXtProt and GENCODE originated from Ensemble, with controlled false discovery rate (FDR). Because of large sized databases used in this pipeline, we found more stringent FDR filtering (0.1% at the peptide level and 1% at the protein level) to claim novel findings, such as GENCODE ASVs and missing proteins, from human hippocampus data set (MSV000081385) and ProteomeXchange (PXD007166). Using our next generation proteomic pipeline (nextPP) with neXtProt and GENCODE databases, two missing proteins such as activity-regulated cytoskeleton-associated protein (ARC, Chr 8) and glutamate receptor ionotropic, kainite 5 (GRIK5, Chr 19) were additionally identified with two or more unique peptides from human brain tissues. Additionally, by applying the pipeline to human brain related data sets such as cortex (PXD000067 and PXD000561), spinal cord, and fetal brain (PXD000561), seven GENCODE ASVs such as ACTN4-012 (Chr.19), DPYSL2-005 (Chr.8), MPRIP-003 (Chr.17), NCAM1-013 (Chr.11), EPB41L1-017 (Chr.20), AGAP1-004 (Chr.2), and CPNE5-005 (Chr.6) were identified from two or more data sets. The identified peptides of GENCODE ASVs were mapped onto novel exon insertions, alternative translations at 5'-untranslated region, or novel protein coding sequence. Applying the pipeline to male reproductive organ related data sets, 52 GENCODE ASVs were identified from two testis (PXD000561 and PXD002179) and a spermatozoa (PXD003947) data sets. Four out of 52 GENCODE ASVs such as RAB11FIP5-008 (Chr. 2), RP13-347D8.7-001 (Chr. X), PRDX4-002 (Chr. X), and RP11-666A8.13-001 (Chr. 17) were identified in all of the three samples.

Direct analysis of site-specific N-glycopeptides of serological proteins in dried blood spot samples.

Dried blood spot (DBS) samples have a number of advantages, especially with respect to ease of collection, transportation, and storage and to reduce biohazard risk. N-glycosylation is a major post-translational modification of proteins in human blood that is related to a variety of biological functions, including metastasis, cell-cell interactions, inflammation, and immunization. Here, we directly analyzed tryptic N-glycopeptides from glycoproteins in DBS samples using liquid chromatography-tandem mass spectrometry (LC-MS/MS) without centrifugation of blood samples, depletion of major proteins, desalting of tryptic peptides, and enrichment of N-glycopeptides. Using this simple method, we identified a total of 41 site-specific N-glycopeptides from 16 glycoproteins in the DBS samples, from immunoglobulin gamma 1 (IgG-1, 10 mg/mL) down to complement component C7 (50 µg/mL). Of these, 32 N-glycopeptides from 14 glycoproteins were consistently quantified over 180 days stored at room temperature. The major abundant glycoproteins in the DBS samples were IgG-1 and IgG-2, which contain nine asialo-fucosylated complex types of 16 different N-glycopeptide isoforms. Sialo-non-fucosylated complex types were primarily detected in the other glycoproteins such as alpha-1-acid glycoprotein 1, 2, alpha-1-antitypsin, alpha-2-macroglobulin, haptoglobin, hemopexin, Ig alpha 1, 2 chain C region, kininogen-1, prothrombin, and serotransferrin. We first report the characterization of site-specific N-glycoproteins in DBS samples by LC-MS/MS with minimal sample preparation.

Characterization of Site-Specific N-Glycopeptide Isoforms of α-1-Acid Glycoprotein from an Interlaboratory Study Using LC-MS/MS.

Glycoprotein conformations are complex and heterogeneous. Currently, site-specific characterization of glycopeptides is a challenge. We sought to establish an efficient method of N-glycoprotein characterization using mass spectrometry (MS). Using alpha-1-acid glycoprotein (AGP) as a model N-glycoprotein, we identified its tryptic N-glycopeptides and examined the data reproducibility in seven laboratories running different LC-MS/MS platforms. We used three test samples and one blind sample to evaluate instrument performance with entire sample preparation workflow. 165 site-specific N-glycopeptides representative of all N-glycosylation sites were identified from AGP 1 and AGP 2 isoforms. The glycopeptide fragmentations by collision-induced dissociation or higher-energy collisional dissociation (HCD) varied based on the MS analyzer. Orbitrap Elite identified the greatest number of AGP N-glycopeptides, followed by Triple TOF and Q-Exactive Plus. Reproducible generation of oxonium ions, glycan-cleaved glycopeptide fragment ions, and peptide backbone fragment ions was essential for successful identification. Laboratory proficiency affected the number of identified N-glycopeptides. The relative quantities of the 10 major N-glycopeptide isoforms of AGP detected in four laboratories were compared to assess reproducibility. Quantitative analysis showed that the coefficient of variation was <25% for all test samples. Our analytical protocol yielded identification and quantification of site-specific N-glycopeptide isoforms of AGP from control and disease plasma sample.

Analysis of fucosylation in liver-secreted N-glycoproteins from human hepatocellular carcinoma plasma using liquid chromatography with tandem mass spectrometry.

Fucosylation of N-glycoproteins has been implicated in various diseases, such as hepatocellular carcinoma (HCC). However, few studies have performed site-specific analysis of fucosylation in liver-secreted proteins. In this study, we characterized the fucosylation patterns of liver-secreted proteins in HCC plasma using a workflow to identify site-specific N-glycoproteins, where characteristic B- and/or Y-ion series with and without fucose in collision-induced dissociation were used in tandem mass spectrometry. In total, 71 fucosylated N-glycopeptides from 13 major liver-secreted proteins in human plasma were globally identified by LC-MS/MS. Additionally, 37 fucosylated N-glycopeptides were newly identified from nine liver-secreted proteins, including alpha-1-antichymotrypsin, alpha-1-antitrypsin, alpha-2-HS-glycoprotein, ceruloplasmin, alpha-1-acid glycoprotein 1/2, alpha-2-macroglobulin, serotransferrin, and beta-2-glycoprotein 1. Of the fucosylated N-glycopeptides, bi- and tri-antennary glycoforms were the most common ones identified in liver-secreted proteins from HCC plasma. Therefore, we suggest that this analytical method is effective for characterizing fucosylation in liver-secreted proteins. Graphical abstract A global map of fucosylated and non-fucosylated glycopeptides from 13 liver-secreted glycoproteins in hepatocellular carcinoma plasma.

Integrated Proteomic Pipeline Using Multiple Search Engines for a Proteogenomic Study with a Controlled Protein False Discovery Rate.

In the Chromosome-Centric Human Proteome Project (C-HPP), false-positive identification by peptide spectrum matches (PSMs) after database searches is a major issue for proteogenomic studies using liquid-chromatography and mass-spectrometry-based large proteomic profiling. Here we developed a simple strategy for protein identification, with a controlled false discovery rate (FDR) at the protein level, using an integrated proteomic pipeline (IPP) that consists of four engrailed steps as follows. First, using three different search engines, SEQUEST, MASCOT, and MS-GF+, individual proteomic searches were performed against the neXtProt database. Second, the search results from the PSMs were combined using statistical evaluation tools including DTASelect and Percolator. Third, the peptide search scores were converted into E-scores normalized using an in-house program. Last, ProteinInferencer was used to filter the proteins containing two or more peptides with a controlled FDR of 1.0% at the protein level. Finally, we compared the performance of the IPP to a conventional proteomic pipeline (CPP) for protein identification using a controlled FDR of <1% at the protein level. Using the IPP, a total of 5756 proteins (vs 4453 using the CPP) including 477 alternative splicing variants (vs 182 using the CPP) were identified from human hippocampal tissue. In addition, a total of 10 missing proteins (vs 7 using the CPP) were identified with two or more unique peptides, and their tryptic peptides were validated using MS/MS spectral pattern from a repository database or their corresponding synthetic peptides. This study shows that the IPP effectively improved the identification of proteins, including alternative splicing variants and missing proteins, in human hippocampal tissues for the C-HPP. All RAW files used in this study were deposited in ProteomeXchange (PXD000395).

Integrated GlycoProteome Analyzer (I-GPA) for Automated Identification and Quantitation of Site-Specific N-Glycosylation.

Human glycoproteins exhibit enormous heterogeneity at each N-glycosite, but few studies have attempted to globally characterize the site-specific structural features. We have developed Integrated GlycoProteome Analyzer (I-GPA) including mapping system for complex N-glycoproteomes, which combines methods for tandem mass spectrometry with a database search and algorithmic suite. Using an N-glycopeptide database that we constructed, we created novel scoring algorithms with decoy glycopeptides, where 95 N-glycopeptides from standard α1-acid glycoprotein were identified with 0% false positives, giving the same results as manual validation. Additionally automated label-free quantitation method was first developed that utilizes the combined intensity of top three isotope peaks at three highest MS spectral points. The efficiency of I-GPA was demonstrated by automatically identifying 619 site-specific N-glycopeptides with FDR ≤ 1%, and simultaneously quantifying 598 N-glycopeptides, from human plasma samples that are known to contain highly glycosylated proteins. Thus, I-GPA platform could make a major breakthrough in high-throughput mapping of complex N-glycoproteomes, which can be applied to biomarker discovery and ongoing global human proteome project.

Chromosome-Based Proteomic Study for Identifying Novel Protein Variants from Human Hippocampal Tissue Using Customized neXtProt and GENCODE Databases.

The goal of the Chromosome-Centric Human Proteome Project (C-HPP) is to fully provide proteomic information from each human chromosome, including novel proteoforms, such as novel protein-coding variants expressed from noncoding genomic regions, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). In the 144 LC/MS/MS raw files from human hippocampal tissues of control, epilepsy, and Alzheimer's disease, we identified the novel proteoforms with a workflow including integrated proteomic pipeline using three different search engines, MASCOT, SEQUEST, and MS-GF+. With a <1% false discovery rate (FDR) at the protein level, the 11 detected peptides mapped to four translated long noncoding RNA variants against the customized databases of GENCODE lncRNA, which also mapped to coding-proteins at different chromosomal sites. We also identified four novel ASVs against the customized databases of GENCODE transcript. The target peptides from the variants were validated by tandem MS fragmentation pattern from their corresponding synthetic peptides. Additionally, a total of 128 SAAVs paired with their wild-type peptides were identified with FDR <1% at the peptide level using a customized database from neXtProt including nonsynonymous single nucleotide polymorphism (nsSNP) information. Among these results, several novel variants related in neuro-degenerative disease were identified using the workflow that could be applicable to C-HPP studies. All raw files used in this study were deposited in ProteomeXchange (PXD000395).

In-depth analysis of site-specific N-glycosylation in vitronectin from human plasma by tandem mass spectrometry with immunoprecipitation.

The characterization of site-specific microheterogeneity in glycoprotein is very important for understanding cell biology and disease processes. Vitronectin is well known to be a multifunctional glycoprotein in the blood and the extracellular matrix, which is related to hepatocellular carcinoma (HCC). Here, we systematically analyzed the site-specific N-glycopeptides of vitronectin in human plasma by tandem mass spectrometry combined with immunoprecipitation and hydrophilic interaction liquid chromatography (HILIC) enrichment. Vitronectin was purified with immunoprecipitation by monoclonal antibody from plasma and digested to tryptic N-glycopeptides.Then, enrichment with HILIC materials was used and followed by analysis with nano-LC/MS/MS. The sequences of N-glycopeptides were identified from the mass spectra by high-energy C-trap dissociation (HCD) and collision-induced dissociation (CID). In HCD mode, oxonium ions were used for recognizing glycopeptides and y ions for sequencing the peptide backbone. In CID mode, Y ions were used for characterizing their glycoforms. As a result, a total of 17 site-specific N-glycopeptides were completely identified in all of the three N-glycosylation sites of vitronectin in human plasma, including 12 N-glycopeptides first reported. Finally, we specifically found that three hybrid and four complex glycopeptides of triantennary forms with outer fucosylation increased in HCC human plasma.

MRM validation of targeted nonglycosylated peptides from N-glycoprotein biomarkers using direct trypsin digestion of undepleted human plasma.

A rapid, simple, and reproducible MRM-based validation method for serological glycoprotein biomarkers in clinical use was developed by targeting the nonglycosylated tryptic peptides adjacent to N-glycosylation sites. Since changes in protein glycosylation are known to be associated with a variety of diseases, glycoproteins have been major targets in biomarker discovery. We previously found that nonglycosylated tryptic peptides adjacent to N-glycosylation sites differed in concentration between normal and hepatocellular carcinoma (HCC) plasma due to differences in steric hindrance of the glycan moiety in N-glycoproteins to tryptic digestion (Lee et al., 2011). To increase the feasibility and applicability of clinical validation of biomarker candidates (nonglycosylated tryptic peptides), we developed a method to effectively monitor nonglycosylated tryptic peptides from a large number of plasma samples and to reduce the total analysis time with maximizing the effect of steric hindrance by the glycans during digestion of glycoproteins. The AUC values of targeted nonglycosylated tryptic peptides were excellent (0.955 for GQYCYELDEK, 0.880 for FEDGVLDPDYPR and 0.907 for TEDTIFLR), indicating that these could be effective biomarkers for hepatocellular carcinoma. This method provides the necessary throughput required to validate glycoprotein biomarkers, as well as quantitative accuracy for human plasma analysis, and should be amenable to clinical use. BIOLOGICAL SIGNIFICANCE: Difficulties in verifying and validating putative protein biomarkers are often caused by complex sample preparation procedures required to determine their concentrations in a large number of plasma samples. To solve the difficulties, we developed MRM-based protein biomarker assays that greatly reduce complex, time-consuming, and less reproducible sample pretreatment steps in plasma for clinical implementation. First, we used undepleted human plasma samples without any enrichment procedures. Using nanoLC/MS/MS, we targeted nonglycosylated tryptic peptides adjacent to N-linked glycosylation sites in N-linked glycoprotein biomarkers, which could be detected in human plasma samples without depleting highly abundant proteins. Second, human plasma proteins were digested with trypsin without reduction and alkylation procedures to minimize sample preparation. Third, trypsin digestion times were shortened so as to obtain reproducible results with maximization of the steric hindrance effect of the glycans during enzyme digestion. Finally, this rapid and simple sample preparation method was applied to validate targeted nonglycosylated tryptic peptides as liver cancer biomarker candidates for diagnosis in 40 normal and 41 hepatocellular carcinoma (HCC) human plasma samples. This strategy provided the necessary throughput required to monitor protein biomarkers, as well as quantitative accuracy in human plasma analysis. From biomarker discovery to clinical implementation, our method will provide a biomarker study platform that is suitable for clinical deployment, and can be applied to high-throughput approaches.

Chromosome 11-centric human proteome analysis of human brain hippocampus tissue.

Human chromosome 11 is the third gene-rich chromosome having 1304 protein-coding genes. According to the GeneCards, this chromosome contains 240 genes related to diseases, as it is well known as a disease-rich chromosome. Although there are many protein-coding genes, the proteomic identification ratio is rather low. As a model study, human hippocampal tissues from patients suffering from Alzheimer's disease and epilepsy were prepared to evaluate the gene-centric statistics related to the gene expression and disorders of chromosome 11. A total of 8828 protein coding genes from brain tissues were extensively off-gel fractionated and profiled by a high resolution mass spectrometer with collision induced dissociation and electron transfer dissociation. Five-hundred twenty-three of the proteins from brain tissues were determined to belong to chromosome 11, representing 37% of the proteins reported in the Global Proteome Machine Database. We extracted gene clusters from a specific biological process or molecular function in gene ontology, among which the olfactory receptor genes showed the largest cluster on chromosome 11. Analysis of the proteome data set from the hippocampus provides a significant network associated with genes and proteins and leads to new insights into the biological and genetic mechanisms of chromosome 11-specific diseases such as Alzheimer's disease.