Wantao Ying

An Approach to Studying Lung Cancer-related Proteins in Human Blood

Early stage lung cancer detection is the first step toward successful clinical therapy and increased patient survival. Clinicians monitor cancer progression by profiling tumor cell proteins in the blood plasma of afflicted patients. Blood plasma, however, is a difficult cancer protein assessment medium because it is rich in albumins and heterogeneous protein species. We report herein a method to detect the proteins released into the circulatory system by tumor cells. Initially we analyzed the protein components in the conditioned medium (CM) of lung cancer primary cell or organ cultures and in the adjacent normal bronchus using one-dimensional PAGE and nano-ESI-MS/MS. We identified 299 proteins involved in key cellular process such as cell growth, organogenesis, and signal transduction. We selected 13 interesting proteins from this list and analyzed them in 628 blood plasma samples using ELISA. We detected 11 of these 13 proteins in the plasma of lung cancer patients and non-patient controls. Our results showed that plasma matrix metalloproteinase 1 levels were elevated significantly in late stage lung cancer patients and that the plasma levels of 14-3-3 σ, β, and η in the lung cancer patients were significantly lower than those in the control subjects. To our knowledge, this is the first time that fascin, ezrin, CD98, annexin A4, 14-3-3 σ, 14-3-3 β, and 14-3-3 η proteins have been detected in human plasma by ELISA. The preliminary results showed that a combination of CD98, fascin, polymeric immunoglobulin receptor/secretory component and 14-3-3 η had a higher sensitivity and specificity than any single marker. In conclusion, we report a method to detect proteins released into blood by lung cancer. This pilot approach may lead to the identification of novel protein markers in blood and provide a new method of identifying tumor biomarker profiles for guiding both early detection and therapy of human cancer.

Proteomic characterization of early‐stage differentiation of mouse embryonic stem cells into neural cells induced by all‐trans retinoic acid in vitro

Embryonic stem (ES) cells are totipotent stem cells, which can differentiate into various kinds of cell types, including neurons. They are widely used as a model system for investigating mechanisms of differentiation events during early mouse development. In this study, proteomic techniques were used to approach the protein profile associated with the early‐stage differentiation of ES cells into neuronal cells induced by all‐trans retinoic acid (ATRA) in vitro. In comparison of the protein profile of parent ES cells with that of ES‐derived neural‐committed cells, which was induced by ATRA for four days, 24 differentially displayed protein spots were selected from two‐dimensional electrophoresis (2‐DE) gels for further protein identification by pepide mass fingerprinting (PMF). Nine proteins were known to being involved in the process of neural differentiation and/or neural survival. Of those, α‐3/α‐7 tubulin and vimentin were downregulated, while cytokeratin 8, cytokeratin 18, G1/S‐special cyclin D2, follistatin‐related protein, NEL protein, platelet‐activating factor acetylhydrolase IB α‐subunit, and thioredoxin peroxidase 2 were upregulated during differentiation of ES cells to neural cells. Additionally, other 12 protein (five upregulated and seven downregulated) spots associated with ES cell differentiation into neuronal cells were not matched to known proteins so far, implicating that they might be novel proteins. The results above indicated that the molecular mechanisms of differentiation of ES cells to neural cells in vitro might be similar to those of other neural systems in vitro and identified that proteomic analysis is an effective strategy to comprehensively unravel the regulatory network of differentiation.

Proteomic analysis on structural proteins of Severe Acute Respiratory Syndrome coronavirus

Recently, a new coronavirus was isolated from the lung tissue of autopsy sample and nasal/throat swabs of the patients with Severe Acute Respiratory Syndrome (SARS) and the causative association with SARS was determined. To reveal further the characteristics of the virus and to provide insight about the molecular mechanism of SARS etiology, a proteomic strategy was utilized to identify the structural proteins of SARS coronavirus (SARS‐CoV) isolated from Vero E6 cells infected with the BJ‐01 strain of the virus. At first, Western blotting with the convalescent sera from SARS patients demonstrated that there were various structural proteins of SARS‐CoV in the cultured supernatant of virus infected‐Vero E6 cells and that nucleocaspid (N) protein had a prominent immunogenicity to the convalescent sera from the patients with SARS, while the immune response of spike (S) protein probably binding with membrane (M) glycoprotein was much weaker. Then, sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) was used to separate the complex protein constituents, and the strategy of continuous slicing from loading well to the bottom of the gels was utilized to search thoroughly the structural proteins of the virus. The proteins in sliced slots were trypsinized in‐gel and identified by mass spectrometry. Three structural proteins named S, N and M proteins of SARS‐CoV were uncovered with the sequence coverage of 38.9, 93.1 and 28.1% respectively. Glycosylation modification in S protein was also analyzed and four glycosylation sites were discovered by comparing the mass spectra before and after deglycosylation of the peptides with PNGase F digestion. Matrix‐assisted laser desorption/ionization‐mass spectrometry determination showed that relative molecular weight of intact N protein is 45 929 Da, which is very close to its theoretically calculated molecular weight 45 935 Da based on the amino acid sequence deduced from the genome with the first amino acid methionine at the N‐terminus depleted and second, serine, acetylated, indicating that phosphorylation does not happen at all in the predicted phosphorylation sites within infected cells nor in virus particles. Intriguingly, a series of shorter isoforms of N protein was observed by SDS‐PAGE and identified by mass spectrometry characterization. For further confirmation of this phenomenon and its related mechanism, recombinant N protein of SARS‐CoV was cleaved in vitro by caspase‐3 and ‐6 respectively. The results demonstrated that these shorter isoforms could be the products from cleavage of caspase‐3 rather than that of caspase‐6. Further, the relationship between the caspase cleavage and the viral infection to the host cell is discussed.

Characterization of glycoprotein digests with hydrophilic interaction chromatography and mass spectrometry

A new hydrophilic interaction chromatography (HILIC) column packed with amide 1.7 μm sorbent was applied to the characterization of glycoprotein digests. Due to the impact of the hydrophilic carbohydrate moiety, glycopeptides were more strongly retained on the column and separated from the remaining nonglycosylated peptides present in the digest. The glycoforms of the same parent peptide were also chromatographically resolved and analyzed using ultraviolet and mass spectrometry detectors. The HILIC method was applied to glyco-profiling of a therapeutic monoclonal antibody and proteins with several N-linked and O-linked glycosylation sites. For characterization of complex proteins with multiple glycosylation sites we utilized 2D LC, where RP separation dimension was used for isolation of glycopeptides and HILIC for resolution of peptide glycoforms. The analysis of site-specific glycan microheterogeneity was illustrated for the CD44 fusion protein.

A Strategy for Precise and Large Scale Identification of Core Fucosylated Glycoproteins

Core fucosylation (CF) patterns of some glycoproteins are more sensitive and specific than evaluation of their total respective protein levels for diagnosis of many diseases, such as cancers. Global profiling and quantitative characterization of CF glycoproteins may reveal potent biomarkers for clinical applications. However, current techniques are unable to reveal CF glycoproteins precisely on a large scale. Here we developed a robust strategy that integrates molecular weight cutoff, neutral loss-dependent MS3, database-independent candidate spectrum filtering, and optimization to effectively identify CF glycoproteins. The rationale for spectrum treatment was innovatively based on computation of the mass distribution in spectra of CF glycopeptides. The efficacy of this strategy was demonstrated by implementation for plasma from healthy subjects and subjects with hepatocellular carcinoma. Over 100 CF glycoproteins and CF sites were identified, and over 10,000 mass spectra of CF glycopeptide were found. The scale of identification results indicates great progress for finding biomarkers with a particular and attractive prospect, and the candidate spectra will be a useful resource for the improvement of database searching methods for glycopeptides.

A Fast Workflow for Identification and Quantification of Proteomes

The current in-depth proteomics makes use of long chromatography gradient to get access to more peptides for protein identification, resulting in covering of as many as 8000 mammalian gene products in 3 days of mass spectrometer running time. Here we report a fast sequencing (Fast-seq) workflow of the use of dual reverse phase high performance liquid chromatography - mass spectrometry (HPLC-MS) with a short gradient to achieve the same proteome coverage in 0.5 day. We adapted this workflow to a quantitative version (Fast quantification, Fast-quan) that was compatible to large-scale protein quantification. We subjected two identical samples to the Fast-quan workflow, which allowed us to systematically evaluate different parameters that impact the sensitivity and accuracy of the workflow. Using the statistics of significant test, we unraveled the existence of substantial falsely quantified differential proteins and estimated correlation of false quantification rate and parameters that are applied in label-free quantification. We optimized the setting of parameters that may substantially minimize the rate of falsely quantified differential proteins, and further applied them on a real biological process. With improved efficiency and throughput, we expect that the Fast-seq/Fast-quan workflow, allowing pair wise comparison of two proteomes in 1 day may make MS available to the masses and impact biomedical research in a positive way.

Quantitative proteomic signature of liver cancer cells: tissue transglutaminase 2 could be a novel protein candidate of human hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common diseases worldwide, with extremely poor prognosis due to failure in diagnosing it early. Alpha-fetoprotein (AFP) is the only available biomarker for HCC diagnosis; however, its use in the early detection of HCC is limited, especially because about one-third of patients afflicted with HCC have normal levels of serum AFP. Thus, identifying additional biomarkers that may be used in combination with AFP to improve early detection of HCC is greatly needed. A quantitative proteomic analysis approach using stable isotope labeling with amino acids in cell culture (SILAC) combined with LTQ-FT-MS/MS identification was used to explore differentially expressed protein profiles between normal (HL-7702) and cancer (HepG2 and SK-HEP-1) cells. A total of 116 proteins were recognized as potential markers that could distinguish between HCC and normal liver cells. Certain proteins, such as AFP, intercellular adhesion molecule-1 (ICAM-1), IQ motif containing GTPase activating protein 2 (IQGAP2), claudin-1 (CLDN1) and tissue transglutaminase 2 (TGM2), were validated both in multiple cell lines and in 61 specimens of clinical HCC cases. TGM2 was overexpressed in some of the AFP-deficient HCC cells (SK-HEP-1 and Bel-7402) and in about half of the tumor tissues with low levels of serum AFP (17/32, AFP-negative HCC). Trace amounts of TGM2 were found to be expressed in the samples with high serum AFP (26/29, AFP-positive HCC). Moreover, TGM2 expression in liver tissues showed an inverse correlation with the level of serum AFP in HCC patients. Notably, TGM2 existed in the supernatant of the AFP-deficient SK-HEP-1, SMMC-7721 and HLE cells, and it was found to be induced in AFP-producing cells (HepG2) by specific siRNA silence assay. Serum TGM2 levels of 109 HCC patients and 42 healthy controls were further measured by an established ELISA assay; the levels were significantly higher in HCC patients, and they correlated with the histological grade and tumor size. These data suggest that TGM2 may serve as a novel histological/serologic candidate involved in HCC, especially for the individuals with normal serum AFP. These novel findings may provide important clues to identify new biomarkers of HCC and indirectly improve early detection of the disease.

A Dataset of Human Fetal Liver Proteome Identified by Subcellular Fractionation and Multiple Protein Separation and Identification Technology

A high throughput process including subcellular fractionation and multiple protein separation and identification technology allowed us to establish the protein expression profile of human fetal liver, which was composed of at least 2,495 distinct proteins and 568 non-isoform groups identified from 64,960 peptides and 24,454 distinct peptides. In addition to the basic protein identification mentioned above, the MS data were used for complementary identification and novel protein mining. By doing the analysis with integrated protein, expressed sequence tag, and genome datasets, 223 proteins and 15 peptides were complementarily identified with high quality MS/MS data.

Combination of Improved 18O Incorporation and Multiple Reaction Monitoring: A Universal Strategy for Absolute Quantitative Verification of Serum Candidate Biomarkers of Liver Cancer

Stable isotope dilution-multiple reaction monitoring-mass spectrometry (SID-MRM-MS), which is an alternative to immunoassay methods such as ELISA and Western blotting, has been used to alleviate the bottlenecks of high-throughput verification of biomarker candidates recently. However, the inconvenience and high isotope consumption required to obtain stably labeled peptide impedes the broad application of this method. In our study, the (18)O-labeling method was introduced to generate stable isotope-labeled peptides instead of the Fmoc chemical synthesis and Qconcat recombinant protein synthesis methods. To make (18)O-labeling suitable for absolute quantification, we have added the following procedures: (1) RapiGest SF and microwave heating were added to increase the labeling efficiency; (2) trypsin was deactivated completely by chemical modification using tris(2-carboxyethyl)phosphine (TCEP) and iodoacetamide (IAA) to prevent back-exchange of (18)O to (16)O, and (3) MRM parameters were optimized to maximize specificity and better distinguish between (18)O-labeled and unlabeled peptides. As a result, the (18)O-labeled peptides can be prepared in less than 1 h with satisfactory efficiency (>97%) and remained stable for 1 week, compared to traditional protocols that require 5 h for labeling with poor stability. Excellent separation of (18)O-labeled and unlabeled peptides was achieved by the MRM-MS spectrum. Finally, through the combined improvement in (18)O-labeling with multiple reaction monitoring, an absolute quantification strategy was developed to quantitatively verify hepatocellular carcinoma-related biomarker candidates, namely, vitronectin and clusterin, in undepleted serum samples. Sample preparation and capillary-HPLC analysis were optimized for high-throughput applications. The reliability of this strategy was further evaluated by method validation, with accuracy (%RE) and precision (%RSD) of less than 20% and good linearity (r(2) > 0.99), and clinical validation, which were consistent with previously reported results. In summary, our strategy can promote broader application of SID-MRM-MS for biomarkers from discovery to verification regarding the significant advantages of the convenient and flexible generation of internal standards, the reduction in the sample labeling steps, and the simple transition.

Brain-specific Proteins Decline in the Cerebrospinal Fluid of Humans with Huntington Disease

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.