Hyoung Joo Lee

The Chromosome-Centric Human Proteome Project for cataloging proteins encoded in the genome

The Chromosome-Centric Human Proteome Project for cataloging proteins encoded in the genome

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.

Holographic display based on a spatial DMD array.

The image space of the reconstructed image from the hologram displayed on a digital micromirror device (DMD) is defined by the diffraction pattern induced by the 2D pixel pattern of the DMD, which works as a 2D blazed grating. Within this space, a reconstructed image of 100 mm × 20 mm is spatially multiplexed by a 2 × 5 DMD array that is aligned on a board, without using any extra optics. Each DMD chip reconstructs an image piece of the size 20 mm (width) × 10 mm (height). The reconstructed image looks somewhat noisy but regenerates the original object image faithfully.

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.

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.

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.

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 Alzheimers 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 Alzheimers disease.

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.

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 peptide quantification using spectral count clustering and cluster abundance

BackgroundQuantification 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 peptides 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.ResultsWe 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.ConclusionsWe 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.