Browsing by Author "Chen, Yu-Ju"

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An Informatics-assisted Label-free Approach for Personalized Tissue Membrane Proteomics: Case Study on Colorectal Cancer
(2011-04-01)
Han, Chia-Li
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Chen, Jinn-Shiun
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Chan, Err-Cheng
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Wu, Chien-Peng
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Yu, Kun-Hsing
We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and reproducible (34% relative S. D.) quantification of three independently purified membrane fractions from the same human colorectal cancer (CRC) tissue. Using CRC as a model, we constructed the personalized membrane protein atlas of paired tumor and adjacent normal tissues from 28 patients with different stages of CRC. Without fractionation, this strategy confidently quantified 856 proteins (>= 2 unique peptides) across different patients, including the first and robust detection (Mascot score: 22,074) of the well-documented CRC marker, carcinoembryonic antigen 5 by a discovery-type proteomics approach. Further validation of a panel of proteins, annexin A4, neutrophils defensin A1, and claudin 3, confirmed differential expression levels and high occurrences (48-70%) in 60 CRC patients. The most significant discovery is the overexpression of stomatin-like 2 (STOML2) for early diagnostic and prognostic potential. Increased expression of STOML2 was associated with decreased CRC-related survival; the mean survival period was 34.77 +/- 2.03 months in patients with high STOML2 expression, whereas 53.67 +/- 3.46 months was obtained for patients with low STOML2 expression. Further analysis by ELISA verified that plasma concentrations of STOML2 in early-stage CRC patients were elevated as compared with those of healthy individuals (p < 0.001), suggesting that STOML2 may be a noninvasive serological biomarker for early CRC diagnosis. The overall sensitivity of STOML2 for CRC detection was 71%, which increased to 87% when combined with CEA measurements. This study demonstrated a sensitive, label-free strategy for differential analysis of tissue membrane proteome, which may provide a roadmap for the subsequent identification of molecular target candidates of multiple cancer types. Molecular & Cellular Proteomics 10: 10.1074/mcp.M110.003087, 1-15, 2011.
dbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications
(2013-01-01)
Lu, Cheng-Tsung
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Huang, Kai-Yao
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Su, Min-Gang
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Lee, Tzong-Yi
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Bretana, Neil Arvin
Protein modification is an extremely important post-translational regulation that adjusts the physical and chemical properties, conformation, stability and activity of a protein; thus altering protein function. Due to the high throughput of mass spectrometry (MS)-based methods in identifying site-specific post-translational modifications (PTMs), dbPTM (http://dbPTM.mbc.nctu.edu.tw/) is updated to integrate experimental PTMs obtained from public resources as well as manually curated MS/MS peptides associated with PTMs from research articles. Version 3.0 of dbPTM aims to be an informative resource for investigating the substrate specificity of PTM sites and functional association of PTMs between substrates and their interacting proteins. In order to investigate the substrate specificity for modification sites, a newly developed statistical method has been applied to identify the significant substrate motifs for each type of PTMs containing sufficient experimental data. According to the data statistics in dbPTM, > 60% of PTM sites are located in the functional domains of proteins. It is known that most PTMs can create binding sites for specific protein-interaction domains that work together for cellular function. Thus, this update integrates protein-protein interaction and domain-domain interaction to determine the functional association of PTM sites located in protein-interacting domains. Additionally, the information of structural topologies on transmembrane (TM) proteins is integrated in dbPTM in order to delineate the structural correlation between the reported PTM sites and TM topologies. To facilitate the investigation of PTMs on TM proteins, the PTM substrate sites and the structural topology are graphically represented. Also, literature information related to PTMs, orthologous conservations and substrate motifs of PTMs are also provided in the resource. Finally, this version features an improved web interface to facilitate convenient access to the resource.
dbSNO: a database of cysteine S-nitrosylation
(2012-09-01)
Lee, Tzong-Yi
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Chen, Yi-Ju
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Lu, Cheng-Tsung
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Ching, Wei-Chieh
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Teng, Yu-Chuan
SUMMARY: S-nitrosylation (SNO), a selective and reversible protein post-translational modification that involves the covalent attachment of nitric oxide (NO) to the sulfur atom of cysteine, critically regulates protein activity, localization and stability. Due to its importance in regulating protein functions and cell signaling, a mass spectrometry-based proteomics method rapidly evolved to increase the dataset of experimentally determined SNO sites. However, there is currently no database dedicated to the integration of all experimentally verified S-nitrosylation sites with their structural or functional information. Thus, the dbSNO database is created to integrate all available datasets and to provide their structural analysis. Up to April 15, 2012, the dbSNO has manually accumulated >3000 experimentally verified S-nitrosylated peptides from 219 research articles using a text mining approach. To solve the heterogeneity among the data collected from different sources, the sequence identity of these reported S-nitrosylated peptides are mapped to the UniProtKB protein entries. To delineate the structural correlation and consensus motif of these SNO sites, the dbSNO database also provides structural and functional analyses, including the motifs of substrate sites, solvent accessibility, protein secondary and tertiary structures, protein domains and gene ontology.
Identification of SEC61 beta and its autoantibody as biomarkers for colorectal cancer
(2011-05-12)
Fan, Chung-Wei
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Chan, Chung-Chuan
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Chen, Kuei-Tien
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Twu, Jen
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Huang, Ya-Shu
Background: To identify novel serological biomarkers for human colorectal cancer (CRC). we analyzed CRC tissues using gel-assisted digestion and isobaric tags with related and absolute quantitation (iTRAQ) labeling mass spectrometry (MS). By comparing pairs of tumor tissues and matched normal tissues. we discovered the SEC61 beta with expression changes 3.3-fold and a marginal statistical significance (p=0.052) previously. Methods: SEC61 beta expression in CRC tissues was further analyzed by western blotting and immunohistochemistry. We next assessed the putative diagnostic value of the SEC61 beta autoantibody as a serum marker. Results: Using western blotting analysis. SEC61 beta expression was increased 1.9-fold in tumor tissues. Immunohistochemical analysis of 64 CRC specimens showed that SEC61 beta was positively detected in 64% of the tumors, but weakly or not detected in > 80% of the adjacent nontumor epithelial cells. Western blot analysis with plasma samples showed that the sensitivity and specificity of the SEC61 beta autoantibody from patients with CRC were 79% and 75%. respectively. Importantly, the results of the SEC61 beta autoantibody for early detection of colorectal cancer revealed a higher sensitivity of 77% than the carcinoembryonic antigen (CEA) assay. Conclusions: Measurement of SEC61 beta autoantibody levels may provide an alternative detection indicator for CRC, particularly among early-stage patients. (C) 2011 Elsevier B.V. All rights reserved.
iTop-Q: an Intelligent Tool for Top-down Proteomics Quantitation Using DYAMOND Algorithm
(2017-12-19)
Chang, Hui-Yin
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Chen, Ching-Tai
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Ko, Chu-Ling
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Chen, Yi-Ju
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Chen, Yu-Ju
Top-down proteomics using liquid chromatogram coupled with mass spectrometry has been increasingly applied for analyzing intact proteins to study genetic variation, alternative splicing, and post-translational modifications (PTMs) of the proteins (proteoforms). However, only a few tools have been developed for charge state deconvolution, monoisotopic/average molecular weight determination and quantitation of proteoforms from LC-MS1 spectra. Though Decon2LS and MASH Suite Pro have been available to provide intraspectrum charge state deconvolution and quantitation, manual processing is still required to quantify proteoforms across multiple MS1 spectra. An automated tool for interspectrum quantitation is a pressing need. Thus, in this paper, we present a user-friendly tool, called iTop-Q (intelligent Top-down Proteomics Quantitation), that automatically performs large-scale proteoform quantitation based on interspectrum abundance in top-down proteomics. Instead of utilizing single spectrum for proteoform quantitation, iTop-Q constructs extracted ion chromatograms (XICs) of possible proteoform peaks across adjacent MS' spectra to calculate abundances for accurate quantitation. Notably, iTop-Q is implemented with a newly proposed algorithm, called DYAMOND, using dynamic programming for charge state deconvolution. In addition, iTop-Qperforms proteoform alignment to support quantitation analysis across replicates/samples. The performance evaluations on an in-house standard data set and a public large-scale yeast lysate data set show that iTop-Q achieves highly accurate quantitation, more consistent quantitation than using intraspectrum quantitation. Furthermore, the DYAMOND algorithm is suitable for high charge state deconvolution and can distinguish shared peaks in coeluting proteoforms. iTop-Qis publicly available for download at http://ms.iis.sinica.edu.tw/COmics/Software_iTop-Q.
Nanomaterial Based Affinity Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry for Biomolecules and Pathogenic Bacteria
(2007-06-01)
Chiu, Tai-Chia
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Huang, Li-Shing
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Lin, Po-Chiao
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Chen, Yu-Chie
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Chen, Yu-Ju
This paper describes mass spectrometry (MS) using nanoparticles (NPs) for the analysis of biomolecules such as aminothiol compounds, drugs, peptides, proteins, and bacteria. Papers and patents dealing with preparation and use of several NPs in MS have been briefly reviewed, including carbon nanotubes, gold NPs, and magnetic NPs. The NPs or bioconjugated NPs were used for selective concentration and/or assisted matrices for desorption and ionization of analytes of interest. When compared to conventional organic matrixes, the NPs provide low MS background in low-mass region and low shot-to-shot variation. MS techniques using NPs and bioconjugated NPs for the analysis of disease-associated biomarkers and bacteria in real samples such as blood and urine are highlighted, showing the advantages of high sensitivity, reproducibility, and simplicity.
Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression
(2020-07-09)
Chen, Yi-Ju
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Roumeliotis, Theodoros, I
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Chang, Ya-Hsuan
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Chen, Ching-Tai
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Han, Chia-Li
Lung cancer in East Asia is characterized by a high percentage of never-smokers, early onset and predominant EGFR mutations. To illuminate the molecular phenotype of this demographically distinct disease, we performed a deep comprehensive proteogenomic study on a prospectively collected cohort in Taiwan, representing early stage, predominantly female, non-smoking lung adenocarcinoma. Integrated genomic, proteomic, and phosphoproteomic analysis delineated the demographically distinct molecular attributes and hallmarks of tumor progression. Mutational signature analysis revealed age- and gender-related mutagenesis mechanisms, characterized by high prevalence of APOBEC mutational signature in younger females and over-representation of environmental carcinogen-like mutational signatures in older females. A proteomics-informed classification distinguished the clinical characteristics of early stage patients with EGFR mutations. Furthermore, integrated protein network analysis revealed the cellular remodeling underpinning clinical trajectories and nominated candidate biomarkers for patient stratification and therapeutic intervention. This multi-omic molecular architecture may help develop strategies for management of early stage never-smoker lung adenocarcinoma.
RegPhos 2.0: an updated resource to explore protein kinase-substrate phosphorylation networks in mammals
(2014-04-25)
Huang, Kai-Yao
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Wu, Hsin-Yi
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Chen, Yi-Ju
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Lu, Cheng-Tsung
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Su, Min-Gang
Protein phosphorylation catalyzed by kinases plays crucial roles in regulating a variety of intracellular processes. Owing to an increasing number of in vivo phosphorylation sites that have been identified by mass spectrometry (MS)-based proteomics, the RegPhos, available online at http://csb.cse.yzu.edu.tw/RegPhos2/, was developed to explore protein phosphorylation networks in human. In this update, we not only enhance the data content in human but also investigate kinase-substrate phosphorylation networks in mouse and rat. The experimentally validated phosphorylation sites as well as their catalytic kinases were extracted from public resources, and MS/MS phosphopeptides were manually curated from research articles. RegPhos 2.0 aims to provide a more comprehensive view of intracellular signaling networks by integrating the information of metabolic pathways and protein-protein interactions. A case study shows that analyzing the phosphoproteome profile of time-dependent cell activation obtained from Liquid chromatography-mass spectrometry (LC-MS/MS) analysis, the RegPhos deciphered not only the consistent scheme in B cell receptor (BCR) signaling pathway but also novel regulatory molecules that may involve in it. With an attempt to help users efficiently identify the candidate biomarkers in cancers, 30 microarray experiments, including 39 cancerous versus normal cells, were analyzed for detecting cancer-specific expressed genes coding for kinases and their substrates. Furthermore, this update features an improved web interface to facilitate convenient access to the exploration of phosphorylation networks for a group of genes/proteins. Database URL: http://csb.cse.yzu.edu.tw/RegPhos2/
Sequential one-pot enzymatic synthesis of oligo-N-acetyllactosamine and its multi-sialylated extensions
(2014)
Chien, Wei-Ting
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Liang, Chien-Fu
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Yu, Ching-Ching
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Lin, Chien-Hung
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Li, Si-Peng
A simple and efficient protocol for the preparative-scale synthesis of various lengths of oligo-N-acetyllactosamine (oligo-LacNAc) and its multi-sialylated extensions is described. The strategy utilizes one thermophilic bacterial thymidylyltransferase (RmlA) coupled with corresponding sugar-1-phosphate kinases to generate two uridine diphosphate sugars, UDP-galactose and UDP-N-acetylglucosamine. By incorporating glycosyltransferases, oligo-LacNAcs and their sialylated analogs were synthesized.
SNOSite: Exploiting Maximal Dependence Decomposition to Identify Cysteine S-Nitrosylation with Substrate Site Specificity
(2011-07-15)
Lee, Tzong-Yi
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Chen, Yi-Ju
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Lu, Tsung-Cheng
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Huang, Hsien-Da
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Chen, Yu-Ju
S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-nitrosylation remains unknown. Based on a total of 586 experimentally identified S-nitrosylation sites from SNAP/L-cysteine-stimulated mouse endothelial cells, this work presents an informatics investigation on S-nitrosylation sites including structural factors such as the flanking amino acids composition, the accessible surface area (ASA) and physicochemical properties, i.e. positive charge and side chain interaction parameter. Due to the difficulty to obtain the conserved motifs by conventional motif analysis, maximal dependence decomposition (MDD) has been applied to obtain statistically significant conserved motifs. Support vector machine (SVM) is applied to generate predictive model for each MDD-clustered motif. According to five-fold cross-validation, the MDD-clustered SVMs could achieve an accuracy of 0.902, and provides a promising performance in an independent test set. The effectiveness of the model was demonstrated on the correct identification of previously reported S-nitrosylation sites of Bos taurus dimethylarginine dimethylaminohydrolase 1 (DDAH1) and human hemoglobin subunit beta (HBB). Finally, the MDD-clustered model was adopted to construct an effective web-based tool, named SNOSite (http://csb.cse.yzu.edu.tw/SNOSite/), for identifying S-nitrosylation sites on the uncharacterized protein sequences.