Liver disease-associated keratin 8 and 18 mutations modulate keratin acetylation and methylation.

Keratin 8 (K8) and keratin 18 (K18) are the intermediate filament proteins whose phosphorylation/transamidation associate with their aggregation in Mallory-Denk bodies found in patients with various liver diseases. However, the functions of other post-translational modifications in keratins related to liver diseases have not been fully elucidated. Here, using a site-specific mutation assay combined with nano-liquid chromatography-tandem mass spectrometry, we identified K8-Lys108 and K18-Lys187/426 as acetylation sites, and K8-Arg47 and K18-Arg55 as methylation sites. Keratin mutation (Arg-to-Lys/Ala) at the methylation sites, but not the acetylation sites, led to decreased stability of the keratin protein. We compared keratin acetylation/methylation in liver disease-associated keratin variants. The acetylation of K8 variants increased or decreased to various extents, whereas the methylation of K18-del65-72 and K18-I150V variants increased. Notably, the highly acetylated/methylated K18-I150V variant was less soluble and exhibited unusually prolonged protein stability, which suggests that additional acetylation of highly methylated keratins has a synergistic effect on prolonged stability. Therefore, the different levels of acetylation/methylation of the liver disease-associated variants regulate keratin protein stability. These findings extend our understanding of how disease-associated mutations in keratins modulate keratin acetylation and methylation, which may contribute to disease pathogenesis.-Jang, K.-H., Yoon, H.-N., Lee, J., Yi, H., Park, S.-Y., Lee, S.-Y., Lim, Y., Lee, H.-J., Cho, J.-W., Paik, Y.-K., Hancock, W. S., Ku, N.-O. Liver disease-associated keratin 8 and 18 mutations modulate keratin acetylation and methylation.

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.

Epsilon-Q: An Automated Analyzer Interface for Mass Spectral Library Search and Label-Free Protein Quantification.

Mass spectrometry (MS) is a widely used proteome analysis tool for biomedical science. In an MS-based bottom-up proteomic approach to protein identification, sequence database (DB) searching has been routinely used because of its simplicity and convenience. However, searching a sequence DB with multiple variable modification options can increase processing time, false-positive errors in large and complicated MS data sets. Spectral library searching is an alternative solution, avoiding the limitations of sequence DB searching and allowing the detection of more peptides with high sensitivity. Unfortunately, this technique has less proteome coverage, resulting in limitations in the detection of novel and whole peptide sequences in biological samples. To solve these problems, we previously developed the "Combo-Spec Search" method, which uses manually multiple references and simulated spectral library searching to analyze whole proteomes in a biological sample. In this study, we have developed a new analytical interface tool called "Epsilon-Q" to enhance the functions of both the Combo-Spec Search method and label-free protein quantification. Epsilon-Q performs automatically multiple spectral library searching, class-specific false-discovery rate control, and result integration. It has a user-friendly graphical interface and demonstrates good performance in identifying and quantifying proteins by supporting standard MS data formats and spectrum-to-spectrum matching powered by SpectraST. Furthermore, when the Epsilon-Q interface is combined with the Combo-Spec search method, called the Epsilon-Q system, it shows a synergistic function by outperforming other sequence DB search engines for identifying and quantifying low-abundance proteins in biological samples. The Epsilon-Q system can be a versatile tool for comparative proteome analysis based on multiple spectral libraries and label-free quantification.

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.

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 Identifies Potential Regulatory Proteins Involved in Development from Dauer Stage to L4 Stage in Caenorhabditis elegans.

When Caenorhabditis elegans encounters unfavorable growth conditions, it enters the dauer stage, an alternative L3 developmental period. A dauer larva resumes larval development to the normal L4 stage by uncharacterized postdauer reprogramming (PDR) when growth conditions become more favorable. During this transition period, certain heterochronic genes involved in controlling the proper sequence of developmental events are known to act, with their mutations suppressing the Muv (multivulva) phenotype in C. elegans. To identify the specific proteins in which the Muv phenotype is highly suppressed, quantitative proteomic analysis with iTRAQ labeling of samples obtained from worms at L1 + 30 h (for continuous development [CD]) and dauer recovery +3 h (for postdauer development [PD]) was carried out to detect changes in protein abundance in the CD and PD states of both N2 and lin-28(n719). Of the 1661 unique proteins identified with a < 1% false discovery rate at the peptide level, we selected 58 proteins exhibiting ≥2-fold up-regulation or ≥2-fold down-regulation in the PD state and analyzed the Gene Ontology terms. RNAi assays against 15 selected up-regulated genes showed that seven genes were predicted to be involved in higher Muv phenotype (p < 0.05) in lin-28(n791), which is not seen in N2. Specifically, two genes, K08H10.1 and W05H9.1, displayed not only the highest rate (%) of Muv phenotype in the RNAi assay but also the dauer-specific mRNA expression, indicating that these genes may be required for PDR, leading to the very early onset of dauer recovery. Thus, our proteomic approach identifies and quantitates the regulatory proteins potentially involved in PDR in C. elegans, which safeguards the overall lifecycle in response to environmental changes.

gFinder: A Web-Based Bioinformatics Tool for the Analysis of N-Glycopeptides.

Glycoproteins influence numerous indispensable biological functions, and changes in protein glycosylation have been observed in various diseases. The identification and characterization of glycoprotein and glycosylation sites by mass spectrometry (MS) remain challenging tasks, and great efforts have been devoted to the development of proteome informatics tools that facilitate the MS analysis of glycans and glycopeptides. Here we report on the development of gFinder, a web-based bioinformatics tool that analyzes mixtures of native N-glycopeptides that have been profiled by tandem MS. gFinder not only enables the simultaneous integration of collision-induced dissociation (CID) and high-energy collisional dissociation (HCD) fragmentation but also merges the spectra for high-throughput analysis. These merged spectra expedite the identification of both glycans and N-glycopeptide backbones in tandem MS data using the glycan database and a proteomic search tool (e.g., Mascot). These data can be used to simultaneously characterize peptide backbone sequences and possible N-glycan structures using assigned scores. gFinder also provides many convenient functions that make it easy to perform manual calculations while viewing the spectrum on-screen. We used gFinder to detect an additional protein (Q8N9B8) that was missed from the previously published data set containing N-linked glycosylation. For N-glycan analysis, we used the GlycomeDB glycan structure database, which integrates the structural and taxonomic data from all of the major carbohydrate databases available in the public domain. Thus, gFinder is a convenient, high-throughput analytical tool for interpreting the tandem mass spectra of N-glycopeptides, which can then be used for identification of potential missing proteins having glycans. gFinder is available publicly at http://gFinder.proteomix.org/ .

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).

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.

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.

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).

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.

NSBP-1 mediates the effects of cholesterol on insulin/IGF-1 signaling in Caenorhabditis elegans.

Nematode sterol-binding protein 1 (NSBP-1) is a homolog of nucleosome assembly protein 1 in mammals that is expressed widely in Caenorhabditis elegans. NSBP-1 mutants are biologically lethal, demonstrating the significance of the gene in growth and development. We investigated how cholesterol influences the insulin signaling pathway through this novel sterol-binding protein in C. elegans. Here we report that NSBP-1 influences many biological processes mediated by insulin signaling, such as longevity, dauer formation, fat storage, and resistance to oxidative stress. We found that NSBP-1 is phosphorylated by AKT-1 downstream of insulin signaling. In the absence of insulin signaling, NSBP-1 is translocated to the nucleus and binds to DAF-16, a FOXO transcription factor, in a cholesterol-dependent manner. Moreover, NSBP-1 and DAF-16 regulate a common set of genes that can directly modulate fat storage, longevity, and resistance to stress. Together, our results present a new steroid-binding molecule that can connect sterol signaling to insulin signaling through direct interaction with FOXO.

GenomewidePDB, a proteomic database exploring the comprehensive protein parts list and transcriptome landscape in human chromosomes.

In an effort to map the human proteome, the Chromosome-centric Human Proteome Project (C-HPP) was recently initiated. As a member of the international consortium working on this project, our laboratory developed a gene-centric proteomic database called GenomewidePDB, which integrates proteomic data for proteins encoded by chromosomes with transcriptomic data and other information from public databases. As an example case, we chose chromosome 13, which is the largest acrocentric human chromosome with the lowest gene density and contains 326 predicted proteins. All proteins stored in GenomewidePDB are linked to other resources, including neXtProt and Ensembl for protein and gene information, respectively. The Global Proteome Machine database (GPMdb) and the PeptideAtlas are also accessed for observed mass spectrometry (MS) information, while Human Protein Atlas is used for information regarding antibody availability and tissue expression, respectively. Gene ontology disease information is also included. As a pilot work, we constructed this GenomewidePDB with the identified 3615 proteins including 53 chromosome 13-origin proteins that are present in normal human placenta tissue. Thus, developing a comprehensive database containing actual experimental proteomics data will provide a valuable resource for cross chromosomal comparison in the C-HPP community.

Comprehensive genome-wide proteomic analysis of human placental tissue for the Chromosome-Centric Human Proteome Project.

As a starting point of the Chromosome-Centric Human Proteome Project (C-HPP), we established strategies of genome-wide proteomic analysis, including protein identification, quantitation of disease-specific proteins, and assessment of post-translational modifications, using paired human placental tissues from healthy and preeclampsia patients. This analysis resulted in identification of 4239 unique proteins with high confidence (two or more unique peptides with a false discovery rate less than 1%), covering 21% of approximately 20, 059 (Ensembl v69, Oct 2012) human proteins, among which 28 proteins exhibited differentially expressed preeclampsia-specific proteins. When these proteins are assigned to all human chromosomes, the pattern of the newly identified placental protein population is proportional to that of the gene count distribution of each chromosome. We also identified 219 unique N-linked glycopeptides, 592 unique phosphopeptides, and 66 chromosome 13-specific proteins. In particular, protein evidence of 14 genes previously known to be specifically up-regulated in human placenta was verified by mass spectrometry. With respect to the functional implication of these proteins, 38 proteins were found to be involved in regulatory factor biosynthesis or the immune system in the placenta, but the molecular mechanism of these proteins during pregnancy warrants further investigation. As far as we know, this work produced the highest number of proteins identified in the placenta and will be useful for annotating and mapping all proteins encoded in the human genome.

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.

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.

Standard guidelines for the chromosome-centric human proteome project.

The objective of the international Chromosome-Centric Human Proteome Project (C-HPP) is to map and annotate all proteins encoded by the genes on each human chromosome. The C-HPP consortium was established to organize a collaborative network among the research teams responsible for protein mapping of individual chromosomes and to identify compelling biological and genetic mechanisms influencing colocated genes and their protein products. The C-HPP aims to foster the development of proteome analysis and integration of the findings from related molecular -omics technology platforms through collaborations among universities, industries, and private research groups. The C-HPP consortium leadership has elicited broad input for standard guidelines to manage these international efforts more efficiently by mobilizing existing resources and collaborative networks. The C-HPP guidelines set out the collaborative consensus of the C-HPP teams, introduce topics associated with experimental approaches, data production, quality control, treatment, and transparency of data, governance of the consortium, and collaborative benefits. A companion approach for the Biology and Disease-Driven HPP (B/D-HPP) component of the Human Proteome Project is currently being organized, building upon the Human Proteome Organization's organ-based and biofluid-based initiatives (www.hupo.org/research). The common application of these guidelines in the participating laboratories is expected to facilitate the goal of a comprehensive analysis of the human proteome.