1
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Bao W, Yamasaki T, Nakano M, Nagae M, Kizuka Y. Functions of unique middle loop and C-terminal tail in GnT-III activity and secretion. Biochim Biophys Acta Gen Subj 2025; 1869:130734. [PMID: 39653250 DOI: 10.1016/j.bbagen.2024.130734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Revised: 11/11/2024] [Accepted: 12/05/2024] [Indexed: 12/13/2024]
Abstract
BACKGROUND N-Glycan branching modulates the diversity of protein functions. β1,4-N-acetylglucosaminyltransferase III (GnT-III or MGAT3) produces a unique GlcNAc branch, "bisecting GlcNAc", in N-glycans, and is involved in Alzheimer's disease and cancer. However, the 3D structure and catalytic mechanism of GnT-III are unclear. According to AlphaFold-based structure prediction, GnT-III likely contains two putative disordered segments, a long middle loop (Loop) and a C-terminal tail (Tail). We hypothesized that these segments play important roles in regulating the activity or intracellular behaviors of GnT-III. METHODS We expressed wild-type GnT-III (GnT-III-WT), GnT-III-Loop- and -Tail-deletion mutants in cells. Their in vitro catalytic activity and glycan biosynthesis in cells were examined using high-performance liquid chromatography, UDP-Glo glycosyltransferase assays, and glycomic analysis. Subcellular localization of WT and GnT-III mutants was investigated by immunostaining, and degradation rate and secretion were also examined. RESULTS The Loop-deletion mutant had higher in vitro and in cellulo activity than GnT-III-WT, indicating that Loop suppresses catalytic activity. In contrast, the Tail-deletion mutant showed weaker activity, increased ER localization, and faster degradation than GnT-III-WT, indicating that Tail is required for proper folding. In addition, deletion of Loop led to aberrant shedding of GnT-III, indicating that Loop contains the cleavage site or regulates GnT-III shedding. CONCLUSIONS Loop and Tail of GnT-III play important roles in catalytic activity, folding and shedding. GENERAL SIGNIFICANCE Our results provide further understanding of the catalysis and shedding mechanisms of GnT-III and can help in the development of methods for modifying the levels of bisecting GlcNAc on glycoproteins and in cells.
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Affiliation(s)
- WanXue Bao
- Institute for Glyco-core Research (iGCORE), Gifu University, Gifu 501-1193, Japan
| | - Takahiro Yamasaki
- Institute for Glyco-core Research (iGCORE), Gifu University, Gifu 501-1193, Japan
| | - Miyako Nakano
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashihiroshima 739-8530, Japan
| | - Masamichi Nagae
- Department of Molecular Immunology, Research Institute for Microbial Diseases, Osaka University, Suita 565-0871, Japan; Laboratory of Molecular Immunology, Immunology Frontier Research Center (IFReC), Osaka University, Suita 565-0871, Japan
| | - Yasuhiko Kizuka
- Institute for Glyco-core Research (iGCORE), Gifu University, Gifu 501-1193, Japan.
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2
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Pratyush P, Kc DB. Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures. Methods Mol Biol 2025; 2870:117-151. [PMID: 39543034 DOI: 10.1007/978-1-0716-4213-9_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2024]
Abstract
Posttranslational modifications (PTMs) play a crucial role in modulating the structure, function, localization, and interactions of proteins, with many PTMs being localized within supersecondary structures, such as helical pairs. These modifications can significantly influence the conformation and stability of these structures. For instance, phosphorylation introduces negative charges that alter electrostatic interactions, while acetylation or methylation of lysine residues affects the stability and interactions of alpha helices or beta strands. Given the pivotal role of supersecondary structures in the overall protein architecture, their modulation by PTMs is essential for protein functionality. This chapter explores the latest advancements in predicting sites for the five PTMs (phosphorylation, acetylation, glycosylation, methylation, and ubiquitination) known to be localized within supersecondary structures. The chapter highlights the recent advances in the prediction of these PTM sites, including the use of global contextualized embeddings from protein language models, integration of structural information, utilization of reliable positive and negative sites, and application of contrastive learning. These methodologies and emerging trends offer a roadmap for novel innovations in addressing PTM prediction challenges, particularly those linked to supersecondary structures.
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Affiliation(s)
- Pawel Pratyush
- Computer Science Department, Michigan Technological University, Houghton, MI, USA
- Computer Science Department, Rochester Institute of Technology, Henrietta, NY, USA
| | - Dukka B Kc
- Computer Science Department, Michigan Technological University, Houghton, MI, USA.
- Computer Science Department, Rochester Institute of Technology, Henrietta, NY, USA.
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3
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Nagai-Okatani C, Tomioka A, Tominaga D, Sakaue H, Kuno A, Kaji H. Inter-tissue glycan heterogeneity: site-specific glycoform analysis of mouse tissue N-glycoproteomes using MS1-based glycopeptide detection method assisted by lectin microarray. Anal Bioanal Chem 2024:10.1007/s00216-024-05686-y. [PMID: 39676134 DOI: 10.1007/s00216-024-05686-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2024] [Revised: 11/25/2024] [Accepted: 11/29/2024] [Indexed: 12/17/2024]
Abstract
To understand the biological and pathological functions of protein glycosylation, the glycan heterogeneities for each glycosite in a single glycoprotein need to be elucidated depending on the type and status of cells. For this aim, a reliable strategy is needed to compare site-specific glycoforms of multiple glycoprotein samples in a comprehensive manner. To analyze this "inter-heterogeneity" of samples, we previously introduced an MS1-based glycopeptide detection method, "Glyco-RIDGE." Here, to elucidate inter-tissue glycan heterogeneities, this estimation method was applied to site-specific N-glycoforms of glycoproteins from six normal mouse tissues (liver, kidney, spleen, pancreas, stomach, and testis). The analyses of desialylated glycopeptides estimated 11,325 site-specific N-glycoforms with 239 glycan compositions at 1260 sites (1122 non-redundant core peptides) in 800 glycoproteins, revealing inter-tissue differences in macro-, micro-, and meta-glycan heterogeneities. To obtain detailed information on their glycan features and tissue distribution, the Glyco-RIDGE results were compared with laser microdissection-assisted lectin microarray (LMD-LMA)-based mouse tissue glycome mapping data deposited on LM-GlycomeAtlas, as well as MS-based mouse tissue glycome data deposited on GlycomeAtlas. This integrated approach supported the certainty of Glyco-RIDGE results and suggested that inter-tissue differences exist in glycan motifs, such as alpha-galactose and bisecting N-acetylglucosamine, in both whole tissue glycomes and specific glycoproteins, Anpep and Lamc1. In addition, the comparison with LMD-LMA-based tissue glycome mapping data suggested the possibility of estimating the tissue distribution of the assigned glycans and glycopeptides. Taken together, these findings demonstrate the utility of an integrated approach using MS assisted by LMA for large-scale analyses.
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Affiliation(s)
- Chiaki Nagai-Okatani
- Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan.
| | - Azusa Tomioka
- Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan
| | - Daisuke Tominaga
- Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan
| | - Hiroaki Sakaue
- Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan
| | - Atsushi Kuno
- Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan
| | - Hiroyuki Kaji
- Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan.
- Institute for Glyco-core Research (iGCORE), Nagoya University, Furo-Cho, Chikusa, Nagoya, Aichi, 464-8601, Japan.
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4
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Liu YS, Miao YL, Dou Y, Yang ZH, Sun W, Zhou X, Li Z, Hideki N, Gao XD, Fujita M. Processing of N-glycans in the ER and Golgi influences the production of surface sialylated glycoRNA. Glycoconj J 2024:10.1007/s10719-024-10171-w. [PMID: 39531110 DOI: 10.1007/s10719-024-10171-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 11/01/2024] [Accepted: 11/04/2024] [Indexed: 11/16/2024]
Abstract
Glycoconjugates, including glycans on proteins and lipids, have obtained significant attention due to their critical roles in both intracellular and intercellular biological functions and processes. Notably, recent discoveries have revealed the presence of glycosylated RNAs (glycoRNAs) on cell surfaces. Despite the well-characterized roles of RNA modifications, RNA glycosylation remains relatively unexplored. In this study, we investigate the relationship between N-glycosylation and RNA glycosylation. Using a recombinant Siglec11-Fc as a probe, we detected surface sialylated glycoRNAs in human cell lines and identified their dependency on the catalytic isoforms of the oligosaccharyltransferase (OST) complex, implicating STT3A-dependent protein glycosylation as a predominant contributor for affecting indirect generation of glycoRNAs. Additionally, perturbations in N-glycan biosynthesis pathways or changes in N-glycan structure impact surface sialylated glycoRNA levels, indicating a regulatory role of glycan metabolic pathways in RNA glycosylation. Together, our results underscore the intricate relationship between protein N-glycosylation and processing and RNA biology.
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Affiliation(s)
- Yi-Shi Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, 214122, China.
| | - Yu-Long Miao
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yue Dou
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Ze-Hui Yang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Wenhao Sun
- Key Laboratory for Southwest Microbial Diversity of the Ministry of Education, School of Life Sciences, Yunnan University, Kunming, Yunnan, 650021, China
| | - Xiaoman Zhou
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Zijie Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Nakanishi Hideki
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Xiao-Dong Gao
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Science, Beijing, 100190, China
| | - Morihisa Fujita
- Institute for Glyco-core Research (iGCORE), Gifu University, Gifu, 501-1193, Japan.
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5
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Gutierrez CS, Kassim AA, Gutierrez BD, Raines RT. Sitetack: a deep learning model that improves PTM prediction by using known PTMs. Bioinformatics 2024; 40:btae602. [PMID: 39388212 PMCID: PMC11552626 DOI: 10.1093/bioinformatics/btae602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 10/05/2024] [Accepted: 10/08/2024] [Indexed: 10/15/2024] Open
Abstract
MOTIVATION Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success. RESULTS We evaluated the use of known PTM sites in prediction via sequence-based deep learning algorithms. For each PTM, known locations of that PTM were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of that PTM at a given site. Without labeling known PTMs, our models are on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms. AVAILABILITY AND IMPLEMENTATION Sitetack is available as a web tool at https://sitetack.net; the source code, representative datasets, instructions for local use, and select models are available at https://github.com/clair-gutierrez/sitetack.
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Affiliation(s)
- Clair S Gutierrez
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, United States
| | - Alia A Kassim
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | | | - Ronald T Raines
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, United States
- Koch Institute for Integrated Cancer Research at MIT, Cambridge, MA 02139, United States
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6
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James VK, van der Zon AAM, Escobar EE, Dunham SD, Gargano AFG, Brodbelt JS. Hydrophilic Interaction Chromatography Coupled to Ultraviolet Photodissociation Affords Identification, Localization, and Relative Quantitation of Glycans on Intact Glycoproteins. J Proteome Res 2024; 23:4684-4693. [PMID: 39312773 DOI: 10.1021/acs.jproteome.4c00600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Protein glycosylation is implicated in a wide array of diseases, yet glycoprotein analysis remains elusive owing to the extreme heterogeneity of glycans, including microheterogeneity of some of the glycosites (amino acid residues). Various mass spectrometry (MS) strategies have proven tremendously successful for localizing and identifying glycans, typically utilizing a bottom-up workflow in which glycoproteins are digested to create glycopeptides to facilitate analysis. An emerging alternative is top-down MS that aims to characterize intact glycoproteins to allow precise identification and localization of glycans. The most comprehensive characterization of intact glycoproteins requires integration of a suitable separation method and high performance tandem mass spectrometry to provide both protein sequence information and glycosite localization. Here, we couple ultraviolet photodissociation and hydrophilic interaction chromatography with high resolution mass spectrometry to advance the characterization of intact glycoproteins ranging from 15 to 34 kDa, offering site localization of glycans, providing sequence coverages up to 93%, and affording relative quantitation of individual glycoforms.
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Affiliation(s)
- Virginia K James
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
| | - Annika A M van der Zon
- van 't Hoff Institute for Molecular Science, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
- Centre of Analytical Sciences Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
| | - Edwin E Escobar
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
| | - Sean D Dunham
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
| | - Andrea F G Gargano
- van 't Hoff Institute for Molecular Science, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
- Centre of Analytical Sciences Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
| | - Jennifer S Brodbelt
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
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7
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Wang S, Di Y, Yang Y, Salovska B, Li W, Hu L, Yin J, Shao W, Zhou D, Cheng J, Liu D, Yang H, Liu Y. PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals. CELL REPORTS METHODS 2024; 4:100859. [PMID: 39255793 PMCID: PMC11440062 DOI: 10.1016/j.crmeth.2024.100859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/13/2024] [Accepted: 08/15/2024] [Indexed: 09/12/2024]
Abstract
To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.
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Affiliation(s)
- Shisheng Wang
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yi Di
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Yin Yang
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Barbora Salovska
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Liqiang Hu
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jiahui Yin
- Information Research Institute, Tongji University, Shanghai 200092, China
| | - Wenguang Shao
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong Zhou
- Department of Medicine, Division of Nephrology, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Jingqiu Cheng
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Dan Liu
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China; State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Hao Yang
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA; Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Biomedical Informatics & Data Science, Yale Univeristy School of Medicine, New Haven, CT 06510, USA.
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8
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Cho D, Lee HM, Kim JA, Song JG, Hwang SH, Lee B, Park J, Tran KM, Kim J, Vo PNL, Bae J, Pimt T, Lee K, Gsponer J, Kim HW, Na D. Autoinhibited Protein Database: a curated database of autoinhibitory domains and their autoinhibition mechanisms. Database (Oxford) 2024; 2024:baae085. [PMID: 39192607 DOI: 10.1093/database/baae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/30/2024] [Accepted: 08/05/2024] [Indexed: 08/29/2024]
Abstract
Autoinhibition, a crucial allosteric self-regulation mechanism in cell signaling, ensures signal propagation exclusively in the presence of specific molecular inputs. The heightened focus on autoinhibited proteins stems from their implication in human diseases, positioning them as potential causal factors or therapeutic targets. However, the absence of a comprehensive knowledgebase impedes a thorough understanding of their roles and applications in drug discovery. Addressing this gap, we introduce Autoinhibited Protein Database (AiPD), a curated database standardizing information on autoinhibited proteins. AiPD encompasses details on autoinhibitory domains (AIDs), their targets, regulatory mechanisms, experimental validation methods, and implications in diseases, including associated mutations and post-translational modifications. AiPD comprises 698 AIDs from 532 experimentally characterized autoinhibited proteins and 2695 AIDs from their 2096 homologs, which were retrieved from 864 published articles. AiPD also includes 42 520 AIDs of computationally predicted autoinhibited proteins. In addition, AiPD facilitates users in investigating potential AIDs within a query sequence through comparisons with documented autoinhibited proteins. As the inaugural autoinhibited protein repository, AiPD significantly aids researchers studying autoinhibition mechanisms and their alterations in human diseases. It is equally valuable for developing computational models, analyzing allosteric protein regulation, predicting new drug targets, and understanding intervention mechanisms AiPD serves as a valuable resource for diverse researchers, contributing to the understanding and manipulation of autoinhibition in cellular processes. Database URL: http://ssbio.cau.ac.kr/databases/AiPD.
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Affiliation(s)
- Daeahn Cho
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
| | - Hyang-Mi Lee
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
| | - Ji Ah Kim
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
| | - Jae Gwang Song
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Su-Hee Hwang
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
| | - Bomi Lee
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Jinsil Park
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Kha Mong Tran
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
| | - Jiwon Kim
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Phuong Ngoc Lam Vo
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
| | - Jooeun Bae
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Teerapat Pimt
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
| | - Kangseok Lee
- Department of Life Science, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Jörg Gsponer
- Center for High-Throughput Biology, University of British Columbia, 2125 East Mall, Vancouver, BC V6T 1Z4, Canada
| | - Hyung Wook Kim
- Department of Bio-integrated Science and Technology, College of Life Sciences, Sejong University, Seoul 05006, Republic of Korea
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, South Korea
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9
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Tan Z, Ning L, Cao L, Zhou Y, Li J, Yang Y, Lin S, Ren X, Xue X, Kang H, Li X, Guan F. Bisecting GlcNAc modification reverses the chemoresistance via attenuating the function of P-gp. Theranostics 2024; 14:5184-5199. [PMID: 39267774 PMCID: PMC11388069 DOI: 10.7150/thno.93879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 08/13/2024] [Indexed: 09/15/2024] Open
Abstract
Rationale: Chemoresistance is a key factor contributing to the failure of anti-breast cancer chemotherapy. Although abnormal glycosylation is closely correlated with breast cancer progression, the function of glycoconjugates in chemoresistance remains poorly understood. Methods: Levels and regulatory roles of bisecting N-acetylglucosamine (GlcNAc) in chemoresistant breast cancer cells were determined in vitro and in vivo. Glycoproteomics guided identification of site-specific bisecting GlcNAc on P-glycoprotein (P-gp). Co-immunoprecipitation coupled mass spectrometry (Co-IP-MS) and proximity labelling MS identified the interactome of P-gp, and the biological function of site-specific bisecting GlcNAc was investigated by site/truncation mutation and structural simulations. Results: Bisecting GlcNAc levels were reduced in chemoresistant breast cancer cells, accompanied by an enhanced expression of P-gp. Enhanced bisecting GlcNAc effectively reversed chemoresistance. Mechanical study revealed that bisecting GlcNAc impaired the association between Ezrin and P-gp, leading to a decreased expression of membrane P-gp. Bisecting GlcNAc suppressed VPS4A-mediated P-gp recruitment into microvesicles, and chemoresistance transmission. Structural dynamics analysis suggested that bisecting GlcNAc at Asn494 introduced structural constraints that rigidified the conformation and suppressed the activity of P-gp. Conclusion: Our findings highlight the crucial role of bisecting GlcNAc in chemoresistance and suggest the possibility of reversing chemoresistance by modulating the specific glycosylation in breast cancer therapy.
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Affiliation(s)
- Zengqi Tan
- Institute of Hematology, Provincial Key Laboratory of Biotechnology, School of Medicine, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
| | - Lulu Ning
- College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science & Technology, Xi'an, Shaanxi, 710069, P.R. China
| | - Lin Cao
- Institute of Hematology, Provincial Key Laboratory of Biotechnology, School of Medicine, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
| | - Yue Zhou
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
| | - Jing Li
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
| | - Yunyun Yang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
| | - Shuai Lin
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710069, P.R. China
| | - Xueting Ren
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710069, P.R. China
| | - Xiaobo Xue
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710069, P.R. China
| | - Xiang Li
- Institute of Hematology, Provincial Key Laboratory of Biotechnology, School of Medicine, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
| | - Feng Guan
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology, College of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, P.R. China
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10
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024; 23:2700-2722. [PMID: 38451675 PMCID: PMC11296931 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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11
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Kizuka Y. Regulation of intracellular activity of N-glycan branching enzymes in mammals. J Biol Chem 2024; 300:107471. [PMID: 38879010 PMCID: PMC11328876 DOI: 10.1016/j.jbc.2024.107471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/01/2024] [Accepted: 06/06/2024] [Indexed: 07/07/2024] Open
Abstract
Most proteins in the secretory pathway are glycosylated, and N-glycans are estimated to be attached to over 7000 proteins in humans. As structural variation of N-glycans critically regulates the functions of a particular glycoprotein, it is pivotal to understand how structural diversity of N-glycans is generated in cells. One of the major factors conferring structural variation of N-glycans is the variable number of N-acetylglucosamine branches. These branch structures are biosynthesized by dedicated glycosyltransferases, including GnT-III (MGAT3), GnT-IVa (MGAT4A), GnT-IVb (MGAT4B), GnT-V (MGAT5), and GnT-IX (GnT-Vb, MGAT5B). In addition, the presence or absence of core modification of N-glycans, namely, core fucose (included as an N-glycan branch in this manuscript), synthesized by FUT8, also confers large structural variation on N-glycans, thereby crucially regulating many protein-protein interactions. Numerous biochemical and medical studies have revealed that these branch structures are involved in a wide range of physiological and pathological processes. However, the mechanisms regulating the activity of the biosynthetic glycosyltransferases are yet to be fully elucidated. In this review, we summarize the previous findings and recent updates regarding regulation of the activity of these N-glycan branching enzymes. We hope that such information will help readers to develop a comprehensive overview of the complex system regulating mammalian N-glycan maturation.
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Affiliation(s)
- Yasuhiko Kizuka
- Institute for Glyco-core Research (iGCORE), Gifu University, Gifu, Japan.
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12
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Hu Y, Schnaubelt M, Chen L, Zhang B, Hoang T, Lih TM, Zhang Z, Zhang H. MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses. Anal Chem 2024; 96:10145-10151. [PMID: 38869158 DOI: 10.1021/acs.analchem.3c01497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.
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Affiliation(s)
- Yingwei Hu
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Michael Schnaubelt
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Li Chen
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Bai Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Trung Hoang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - T Mamie Lih
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Zhen Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Hui Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
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13
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Xu X, Yin K, Wu R. Systematic Investigation of the Trafficking of Glycoproteins on the Cell Surface. Mol Cell Proteomics 2024; 23:100761. [PMID: 38593903 PMCID: PMC11087972 DOI: 10.1016/j.mcpro.2024.100761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
Glycoproteins located on the cell surface play a pivotal role in nearly every extracellular activity. N-glycosylation is one of the most common and important protein modifications in eukaryotic cells, and it often regulates protein folding and trafficking. Glycosylation of cell-surface proteins undergoes meticulous regulation by various enzymes in the endoplasmic reticulum (ER) and the Golgi, ensuring their proper folding and trafficking to the cell surface. However, the impacts of protein N-glycosylation, N-glycan maturity, and protein folding status on the trafficking of cell-surface glycoproteins remain to be explored. In this work, we comprehensively and site-specifically studied the trafficking of cell-surface glycoproteins in human cells. Integrating metabolic labeling, bioorthogonal chemistry, and multiplexed proteomics, we investigated 706 N-glycosylation sites on 396 cell-surface glycoproteins in monocytes, either by inhibiting protein N-glycosylation, disturbing N-glycan maturation, or perturbing protein folding in the ER. The current results reveal their distinct impacts on the trafficking of surface glycoproteins. The inhibition of protein N-glycosylation dramatically suppresses the trafficking of many cell-surface glycoproteins. The N-glycan immaturity has more substantial effects on proteins with high N-glycosylation site densities, while the perturbation of protein folding in the ER exerts a more pronounced impact on surface glycoproteins with larger sizes. Furthermore, for N-glycosylated proteins, their trafficking to the cell surface is related to the secondary structures and adjacent amino acid residues of glycosylation sites. Systematic analysis of surface glycoprotein trafficking advances our understanding of the mechanisms underlying protein secretion and surface presentation.
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Affiliation(s)
- Xing Xu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Kejun Yin
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Ronghu Wu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA.
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14
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Nataf S, Guillen M, Pays L. The Immunometabolic Gene N-Acetylglucosamine Kinase Is Uniquely Involved in the Heritability of Multiple Sclerosis Severity. Int J Mol Sci 2024; 25:3803. [PMID: 38612613 PMCID: PMC11011344 DOI: 10.3390/ijms25073803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
The clinical severity of multiple sclerosis (MS), an autoimmune disorder of the central nervous system, is thought to be determined by environmental and genetic factors that have not yet been identified. In a recent genome-wide association study (GWAS), a single nucleotide polymorphism (SNP), rs10191329, has been associated with MS severity in two large independent cohorts of patients. Different approaches were followed by the authors to prioritize the genes that are transcriptionally regulated by such an SNP. It was concluded that the identified SNP regulates a group of proximal genes involved in brain resilience and cognitive abilities rather than immunity. Here, by conducting an alternative strategy for gene prioritization, we reached the opposite conclusion. According to our re-analysis, the main target of rs10191329 is N-Acetylglucosamine Kinase (NAGK), a metabolic gene recently shown to exert major immune functions via the regulation of the nucleotide-binding oligomerization domain-containing protein 2 (NOD2) pathway. To gain more insights into the immunometabolic functions of NAGK, we analyzed the currently known list of NAGK protein partners. We observed that NAGK integrates a dense network of human proteins that are involved in glucose metabolism and are highly expressed by classical monocytes. Our findings hold potentially major implications for the understanding of MS pathophysiology.
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Affiliation(s)
- Serge Nataf
- Bank of Tissues and Cells, Hospices Civils de Lyon, Hôpital Edouard Herriot, Place d’Arsonval, F-69003 Lyon, France
- Stem-Cell and Brain Research Institute, 18 Avenue du Doyen Lépine, F-69500 Bron, France
- Lyon-Est School of Medicine, University Claude Bernard Lyon 1, 43 Bd du 11 Novembre 1918, F-69100 Villeurbanne, France
| | - Marine Guillen
- Bank of Tissues and Cells, Hospices Civils de Lyon, Hôpital Edouard Herriot, Place d’Arsonval, F-69003 Lyon, France
- Stem-Cell and Brain Research Institute, 18 Avenue du Doyen Lépine, F-69500 Bron, France
| | - Laurent Pays
- Bank of Tissues and Cells, Hospices Civils de Lyon, Hôpital Edouard Herriot, Place d’Arsonval, F-69003 Lyon, France
- Stem-Cell and Brain Research Institute, 18 Avenue du Doyen Lépine, F-69500 Bron, France
- Lyon-Est School of Medicine, University Claude Bernard Lyon 1, 43 Bd du 11 Novembre 1918, F-69100 Villeurbanne, France
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15
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Pasala C, Sharma S, Roychowdhury T, Moroni E, Colombo G, Chiosis G. N-Glycosylation as a Modulator of Protein Conformation and Assembly in Disease. Biomolecules 2024; 14:282. [PMID: 38540703 PMCID: PMC10968129 DOI: 10.3390/biom14030282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 05/01/2024] Open
Abstract
Glycosylation, a prevalent post-translational modification, plays a pivotal role in regulating intricate cellular processes by covalently attaching glycans to macromolecules. Dysregulated glycosylation is linked to a spectrum of diseases, encompassing cancer, neurodegenerative disorders, congenital disorders, infections, and inflammation. This review delves into the intricate interplay between glycosylation and protein conformation, with a specific focus on the profound impact of N-glycans on the selection of distinct protein conformations characterized by distinct interactomes-namely, protein assemblies-under normal and pathological conditions across various diseases. We begin by examining the spike protein of the SARS virus, illustrating how N-glycans regulate the infectivity of pathogenic agents. Subsequently, we utilize the prion protein and the chaperone glucose-regulated protein 94 as examples, exploring instances where N-glycosylation transforms physiological protein structures into disease-associated forms. Unraveling these connections provides valuable insights into potential therapeutic avenues and a deeper comprehension of the molecular intricacies that underlie disease conditions. This exploration of glycosylation's influence on protein conformation effectively bridges the gap between the glycome and disease, offering a comprehensive perspective on the therapeutic implications of targeting conformational mutants and their pathologic assemblies in various diseases. The goal is to unravel the nuances of these post-translational modifications, shedding light on how they contribute to the intricate interplay between protein conformation, assembly, and disease.
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Affiliation(s)
- Chiranjeevi Pasala
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.P.); (S.S.); (T.R.)
| | - Sahil Sharma
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.P.); (S.S.); (T.R.)
| | - Tanaya Roychowdhury
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.P.); (S.S.); (T.R.)
| | - Elisabetta Moroni
- The Institute of Chemical Sciences and Technologies (SCITEC), Italian National Research Council (CNR), 20131 Milano, Italy; (E.M.); (G.C.)
| | - Giorgio Colombo
- The Institute of Chemical Sciences and Technologies (SCITEC), Italian National Research Council (CNR), 20131 Milano, Italy; (E.M.); (G.C.)
- Department of Chemistry, University of Pavia, 27100 Pavia, Italy
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (C.P.); (S.S.); (T.R.)
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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16
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Garcia-Marques F, Fuller K, Bermudez A, Shamsher N, Zhao H, Brooks JD, Flory MR, Pitteri SJ. Identification and characterization of intact glycopeptides in human urine. Sci Rep 2024; 14:3716. [PMID: 38355753 PMCID: PMC10866872 DOI: 10.1038/s41598-024-53299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Glycoproteins in urine have the potential to provide a rich class of informative molecules for studying human health and disease. Despite this promise, the urine glycoproteome has been largely uncharacterized. Here, we present the analysis of glycoproteins in human urine using LC-MS/MS-based intact glycopeptide analysis, providing both the identification of protein glycosites and characterization of the glycan composition at specific glycosites. Gene enrichment analysis reveals differences in biological processes, cellular components, and molecular functions in the urine glycoproteome versus the urine proteome, as well as differences based on the major glycan class observed on proteins. Meta-heterogeneity of glycosylation is examined on proteins to determine the variation in glycosylation across multiple sites of a given protein with specific examples of individual sites differing from the glycosylation trends in the overall protein. Taken together, this dataset represents a potentially valuable resource as a baseline characterization of glycoproteins in human urine for future urine glycoproteomics studies.
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Affiliation(s)
- Fernando Garcia-Marques
- Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, 3155 Porter Drive MC5483, Palo Alto, CA, 94304, USA
| | - Keely Fuller
- Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, 3155 Porter Drive MC5483, Palo Alto, CA, 94304, USA
| | - Abel Bermudez
- Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, 3155 Porter Drive MC5483, Palo Alto, CA, 94304, USA
| | - Nikhiya Shamsher
- Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, 3155 Porter Drive MC5483, Palo Alto, CA, 94304, USA
| | - Hongjuan Zhao
- Department of Urology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - James D Brooks
- Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, 3155 Porter Drive MC5483, Palo Alto, CA, 94304, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Mark R Flory
- Cancer Early Detection Advanced Research (CEDAR) Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239-3098, USA
| | - Sharon J Pitteri
- Canary Center at Stanford for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, 3155 Porter Drive MC5483, Palo Alto, CA, 94304, USA.
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17
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Liu Z, Wu Z, Zhou Y, Xia J, Zhang W, Gao S, Li S, Lu Z, Zhang X, Yang S. Hydrophilic Peptide and Glycopeptide as Immobilized Sorbents for Glycosylation Analysis. Anal Chem 2024; 96:1498-1505. [PMID: 38216336 DOI: 10.1021/acs.analchem.3c03944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Hydrophilic interaction liquid chromatography (HILIC) is widely used for glycopeptide enrichment in shot-gun glycoproteomics to enhance the glycopeptide signal and minimize the ionization competition of peptides. In this work, we have developed a novel hydrophilic material (glycoHILIC) based on glycopeptides and peptides to provide hydrophilic properties. GlycoHILIC was synthesized by oxidizing cotton and then reacting the resulting aldehyde with the N-terminus of the glycopeptide or peptide by reductive amination. Due to the large amount of hydrophilic carbohydrates and hydrophilic amino acids contained in glycopeptides, glycoHILIC showed significantly better enrichment of glycopeptides than cotton itself. Our results demonstrate that glycoHILIC has high selectivity, a low detection limit, and good stability. Over 257 unique N-linked glycosylation sites in 1477 intact N-glycopeptides from 146 glycoproteins were identified from 1 μL of human serum using glycoHILIC. Serum analysis of pancreatic cancer patients found that 38 N-glycopeptides among 21 glycoproteins changed significantly, of which 7 N-glycopeptides increased and 31 N-glycopeptides decreased. These results demonstrate that glycoHILIC can be used for glycopeptide enrichment and analysis.
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Affiliation(s)
- Zhaoliang Liu
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China
| | - Zhen Wu
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Yufeng Zhou
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China
| | - Jun Xia
- Laboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, China
| | - Wenqi Zhang
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China
| | - Song Gao
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, Jiangsu Ocean University, Lianyungang 222005, China
| | - Shuwei Li
- Nanjing Apollomics Biotech, Inc., Nanjing, Jiangsu 210033, China
| | - Zhaohui Lu
- Health Examination Center, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, China
| | - Xumin Zhang
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Shuang Yang
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China
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18
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Hou X, Wang Y, Bu D, Wang Y, Sun S. EMNGly: predicting N-linked glycosylation sites using the language models for feature extraction. Bioinformatics 2023; 39:btad650. [PMID: 37930896 PMCID: PMC10627407 DOI: 10.1093/bioinformatics/btad650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/14/2023] [Indexed: 11/08/2023] Open
Abstract
MOTIVATION N-linked glycosylation is a frequently occurring post-translational protein modification that serves critical functions in protein folding, stability, trafficking, and recognition. Its involvement spans across multiple biological processes and alterations to this process can result in various diseases. Therefore, identifying N-linked glycosylation sites is imperative for comprehending the mechanisms and systems underlying glycosylation. Due to the inherent experimental complexities, machine learning and deep learning have become indispensable tools for predicting these sites. RESULTS In this context, a new approach called EMNGly has been proposed. The EMNGly approach utilizes pretrained protein language model (Evolutionary Scale Modeling) and pretrained protein structure model (Inverse Folding Model) for features extraction and support vector machine for classification. Ten-fold cross-validation and independent tests show that this approach has outperformed existing techniques. And it achieves Matthews Correlation Coefficient, sensitivity, specificity, and accuracy of 0.8282, 0.9343, 0.8934, and 0.9143, respectively on a benchmark independent test set.
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Affiliation(s)
- Xiaoyang Hou
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Wang
- Syneron Technology, Guangzhou 510000, China
| | - Dongbo Bu
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaojun Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
| | - Shiwei Sun
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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19
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Piniello B, Macías-León J, Miyazaki S, García-García A, Compañón I, Ghirardello M, Taleb V, Veloz B, Corzana F, Miyagawa A, Rovira C, Hurtado-Guerrero R. Molecular basis for bacterial N-glycosylation by a soluble HMW1C-like N-glycosyltransferase. Nat Commun 2023; 14:5785. [PMID: 37723184 PMCID: PMC10507012 DOI: 10.1038/s41467-023-41238-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/24/2023] [Indexed: 09/20/2023] Open
Abstract
Soluble HMW1C-like N-glycosyltransferases (NGTs) catalyze the glycosylation of Asn residues in proteins, a process fundamental for bacterial autoaggregation, adhesion and pathogenicity. However, our understanding of their molecular mechanisms is hindered by the lack of structures of enzymatic complexes. Here, we report structures of binary and ternary NGT complexes of Aggregatibacter aphrophilus NGT (AaNGT), revealing an essential dyad of basic/acidic residues located in the N-terminal all α-domain (AAD) that intimately recognizes the Thr residue within the conserved motif Asn0-X+1-Ser/Thr+2. Poor substrates and inhibitors such as UDP-galactose and UDP-glucose mimetics adopt non-productive conformations, decreasing or impeding catalysis. QM/MM simulations rationalize these results, showing that AaNGT follows a SN2 reaction mechanism in which the acceptor asparagine uses its imidic form for catalysis and the UDP-glucose phosphate group acts as a general base. These findings provide key insights into the mechanism of NGTs and will facilitate the design of structure-based inhibitors to treat diseases caused by non-typeable H. influenzae or other Gram-negative bacteria.
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Affiliation(s)
- Beatriz Piniello
- Departament de Química Inorgànica i Orgànica (Secció de Química Orgànica) and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Martí i Franquès 1, 08028, Barcelona, Spain
| | - Javier Macías-León
- Institute of Biocomputation and Physics of Complex Systems, University of Zaragoza, Mariano Esquillor s/n, Campus Rio Ebro, Edificio I+D, Zaragoza, Spain
| | - Shun Miyazaki
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan
| | - Ana García-García
- Institute of Biocomputation and Physics of Complex Systems, University of Zaragoza, Mariano Esquillor s/n, Campus Rio Ebro, Edificio I+D, Zaragoza, Spain
| | - Ismael Compañón
- Departamento de Química, Universidad de La Rioja, Centro de Investigación en Síntesis Química, E-26006, Logroño, Spain
| | - Mattia Ghirardello
- Departamento de Química, Universidad de La Rioja, Centro de Investigación en Síntesis Química, E-26006, Logroño, Spain
| | - Víctor Taleb
- Institute of Biocomputation and Physics of Complex Systems, University of Zaragoza, Mariano Esquillor s/n, Campus Rio Ebro, Edificio I+D, Zaragoza, Spain
| | - Billy Veloz
- Institute of Biocomputation and Physics of Complex Systems, University of Zaragoza, Mariano Esquillor s/n, Campus Rio Ebro, Edificio I+D, Zaragoza, Spain
| | - Francisco Corzana
- Departamento de Química, Universidad de La Rioja, Centro de Investigación en Síntesis Química, E-26006, Logroño, Spain
| | - Atsushi Miyagawa
- Department of Life Science and Applied Chemistry, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan
| | - Carme Rovira
- Departament de Química Inorgànica i Orgànica (Secció de Química Orgànica) and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Martí i Franquès 1, 08028, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010, Barcelona, Spain.
| | - Ramon Hurtado-Guerrero
- Institute of Biocomputation and Physics of Complex Systems, University of Zaragoza, Mariano Esquillor s/n, Campus Rio Ebro, Edificio I+D, Zaragoza, Spain.
- Copenhagen Center for Glycomics, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.
- Fundación ARAID, 50018, Zaragoza, Spain.
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20
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Antico O, Nirujogi RS, Muqit MM. Whole proteome copy number dataset in primary mouse cortical neurons. Data Brief 2023; 49:109336. [PMID: 37456110 PMCID: PMC10344827 DOI: 10.1016/j.dib.2023.109336] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023] Open
Abstract
The functional diversity of neurons is specified through their proteome resulting in elaborate and tightly regulated protein interaction networks and signalling that regulates neuronal processes. Dysregulation of these dynamic networks in development or in adulthood lead to neurodevelopmental or neurological disorders respectively. Over the past few decades, mass spectrometry has become a powerful tool for quantifying and resolving any proteome, including complex tissues such as the brain proteome, with technological advances leading to higher levels of resolution and throughput than traditional biochemical techniques. In this article, we provide a proteomic reference dataset that has been generated to identify proteins and quantify their level of expression in primary mouse cortical neurons. It represents a summary analysis of previously published data in (Antico et al., 2021). Mouse cortical neurons were isolated from E16.5 C57Bl/6J mice and cultured for 21 days in vitro (DIV). We employed the mitochondrial uncouplers AntimycinA/Oligomycin (AO) to induce mitochondrial depolarisation that is a well-established paradigm to assess mitophagic signalling. Total lysates from mouse primary cortical neurons were subjected to label-free quantitative proteomic analysis using both data dependent acquisition (DDA) and data independent acquisition (DIA) modes. DDA proteomic analysis identified a total dataset of 9367 proteins in mouse cortical neurons and absolute abundance of proteins was calculated as copy numbers per cell. DDA dataset was also processed to generate a reference spectral library to fit in and quantify MS spectra generated in DIA mode. Quantitative DIA analysis identified more than 6000 protein groups and statistical comparison of the two analysed groups (untreated and AO-treated) revealed that the neuronal proteome was largely unchanged post mitochondrial depolarisation for 5 hours. To our knowledge, these files represent the most comprehensive DDA and DIA reference datasets of fully functional maturated mouse primary cortical neurons and serve as a valuable resource for further investigating the role of specific proteins involved in neurobiology and neurological disorders such as Alzheimer's disease (AD), Parkinson's disease (PD) and Autism Spectrum Disorders (ASD).
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Affiliation(s)
- Odetta Antico
- MRC Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee, United Kingdom, DD1 5EH
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Raja S. Nirujogi
- MRC Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee, United Kingdom, DD1 5EH
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Miratul M.K. Muqit
- MRC Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee, United Kingdom, DD1 5EH
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
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21
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Xu S, Wu R. Glycobiology and proteomics: has mass spectrometry moved the field forward? Expert Rev Proteomics 2023; 20:303-307. [PMID: 37667879 PMCID: PMC10841282 DOI: 10.1080/14789450.2023.2255748] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/26/2023] [Indexed: 09/06/2023]
Affiliation(s)
- Senhan Xu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Ronghu Wu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
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22
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Lih TM, Cho KC, Schnaubelt M, Hu Y, Zhang H. Integrated glycoproteomic characterization of clear cell renal cell carcinoma. Cell Rep 2023; 42:112409. [PMID: 37074911 DOI: 10.1016/j.celrep.2023.112409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/03/2023] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC), a common form of RCC, is responsible for the high mortality rate of kidney cancer. Dysregulations of glycoproteins have been shown to associate with ccRCC. However, the molecular mechanism has not been well characterized. Here, a comprehensive glycoproteomic analysis is conducted using 103 tumors and 80 paired normal adjacent tissues. Altered glycosylation enzymes and corresponding protein glycosylation are observed, while two of the major ccRCC mutations, BAP1 and PBRM1, show distinct glycosylation profiles. Additionally, inter-tumor heterogeneity and cross-correlation between glycosylation and phosphorylation are observed. The relation of glycoproteomic features to genomic, transcriptomic, proteomic, and phosphoproteomic changes shows the role of glycosylation in ccRCC development with potential for therapeutic interventions. This study reports a large-scale tandem mass tag (TMT)-based quantitative glycoproteomic analysis of ccRCC that can serve as a valuable resource for the community.
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Affiliation(s)
- T Mamie Lih
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
| | - Kyung-Cho Cho
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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23
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Chau TH, Chernykh A, Kawahara R, Thaysen-Andersen M. Critical considerations in N-glycoproteomics. Curr Opin Chem Biol 2023; 73:102272. [PMID: 36758418 DOI: 10.1016/j.cbpa.2023.102272] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/30/2022] [Accepted: 01/05/2023] [Indexed: 02/10/2023]
Abstract
N-Glycoproteomics, the system-wide study of glycans asparagine-linked to protein carriers, holds a unique and still largely untapped potential to provide deep insights into the complexity and dynamics of the heterogeneous N-glycoproteome. Despite the advent of innovative analytical and informatics tools aiding the analysis, N-glycoproteomics remains challenging and consequently largely restricted to specialised laboratories. Aiming to stimulate discussions of method harmonisation, data standardisation and reporting guidelines to make N-glycoproteomics more reproducible and accessible to the community, we here discuss critical considerations related to the design and execution of N-glycoproteomics experiments and highlight good practices in N-glycopeptide data collection, analysis, interpretation and sharing. Giving the rapid maturation and, expectedly, a wide-spread implementation of N-glycoproteomics capabilities across the community in future years, this piece aims to point out common pitfalls, to encourage good data sharing and documentation practices, and to highlight practical solutions and strategies to enhance the insight into the N-glycoproteome.
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Affiliation(s)
- The Huong Chau
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Anastasia Chernykh
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Rebeca Kawahara
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Morten Thaysen-Andersen
- School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, Australia; Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia; Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan.
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24
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Nemčić M, Tijardović M, Rudman N, Bulum T, Tomić M, Plavša B, Vučković Rebrina S, Vučić Lovrenčić M, Duvnjak L, Morahan G, Gornik O. N-glycosylation of serum proteins in adult type 1 diabetes mellitus exposes further changes compared to children at the disease onset. Clin Chim Acta 2023; 543:117298. [PMID: 36925056 DOI: 10.1016/j.cca.2023.117298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
OBJECTIVE Previously we have shown that plasma protein N-glycosylation is changed in children at the onset of type 1 diabetes. In this study, we aim to identify N-glycan changes in adults with T1DM, compare them to those in children, and investigate their associations with disease duration, complications, glycaemic status, and smoking. METHODS Serum protein N-glycans from 200 adults with type 1 diabetes and 298 healthy controls were analysed using ultra-high performance liquid chromatography and divided into 39 directly measured glycan groups from which 16 derived traits were calculated. RESULTS Compared to healthy controls, subjects with type 1 diabetes showed differences in 19 glycan groups and a decrease in monogalactosylated, an increase in digalactosylated, monosialylated, and antennary fucosylated derived traits, from which changes in monogalactosylation and seven directly measured traits overlapped with previously reported in children. Changes in four directly measured and two derived traits previously seen in children were not detected in adults. HbA1c was positively associated with sialylated and highly branched structures, whereas N-glycome was not influenced by disease duration or diabetic complications. CONCLUSIONS Our results suggest potential N-glycome involvement in different stages of type 1 diabetes, including processes underlying its development, the disease itself, as well as those occurring after disease establishment.
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Affiliation(s)
- Matej Nemčić
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, Croatia.
| | - Marko Tijardović
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, Croatia.
| | - Najda Rudman
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, Croatia.
| | - Tomislav Bulum
- Department of Endocrinology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4A, 10000 Zagreb, Croatia.
| | - Martina Tomić
- Department of Ophthalmology, Vuk Vrhovac University Clinic, Merkur University Hospital, Dugi dol 4A, 10000 Zagreb, Croatia.
| | - Branimir Plavša
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, Croatia.
| | - Sandra Vučković Rebrina
- Department of Endocrinology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4A, 10000 Zagreb, Croatia.
| | - Marijana Vučić Lovrenčić
- Department of Clinical Chemistry and Laboratory Medicine, Merkur University Hospital, Zajčeva ul. 19, 10000 Zagreb, Croatia.
| | - Lea Duvnjak
- Department of Endocrinology, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, Merkur University Hospital, Dugi dol 4A, 10000 Zagreb, Croatia.
| | - Grant Morahan
- Centre for Diabetes Research, The Harry Perkins Institute for Medical Research, 6 Verdun St, Nedlands WA 6009, Perth, Australia; Australian Centre for Accelerating Diabetes Innovations, University of Melbourne, Swanston St, Parkville, VIC 3052, Australia.
| | - Olga Gornik
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, Croatia.
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25
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Bogetofte H, Ryan BJ, Jensen P, Schmidt SI, Vergoossen DLE, Barnkob MB, Kiani LN, Chughtai U, Heon-Roberts R, Caiazza MC, McGuinness W, Márquez-Gómez R, Vowles J, Bunn FS, Brandes J, Kilfeather P, Connor JP, Fernandes HJR, Caffrey TM, Meyer M, Cowley SA, Larsen MR, Wade-Martins R. Post-translational proteomics platform identifies neurite outgrowth impairments in Parkinson's disease GBA-N370S dopamine neurons. Cell Rep 2023; 42:112180. [PMID: 36870058 DOI: 10.1016/j.celrep.2023.112180] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/04/2022] [Accepted: 02/13/2023] [Indexed: 03/05/2023] Open
Abstract
Variants at the GBA locus, encoding glucocerebrosidase, are the strongest common genetic risk factor for Parkinson's disease (PD). To understand GBA-related disease mechanisms, we use a multi-part-enrichment proteomics and post-translational modification (PTM) workflow, identifying large numbers of dysregulated proteins and PTMs in heterozygous GBA-N370S PD patient induced pluripotent stem cell (iPSC) dopamine neurons. Alterations in glycosylation status show disturbances in the autophagy-lysosomal pathway, which concur with upstream perturbations in mammalian target of rapamycin (mTOR) activation in GBA-PD neurons. Several native and modified proteins encoded by PD-associated genes are dysregulated in GBA-PD neurons. Integrated pathway analysis reveals impaired neuritogenesis in GBA-PD neurons and identify tau as a key pathway mediator. Functional assays confirm neurite outgrowth deficits and identify impaired mitochondrial movement in GBA-PD neurons. Furthermore, pharmacological rescue of glucocerebrosidase activity in GBA-PD neurons improves the neurite outgrowth deficit. Overall, this study demonstrates the potential of PTMomics to elucidate neurodegeneration-associated pathways and potential drug targets in complex disease models.
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Affiliation(s)
- Helle Bogetofte
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK; Department of Neurobiology Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense C, Denmark; Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark
| | - Brent J Ryan
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK.
| | - Pia Jensen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark
| | - Sissel I Schmidt
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK; Department of Neurobiology Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense C, Denmark
| | - Dana L E Vergoossen
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK
| | - Mike B Barnkob
- Centre for Cellular Immunotherapy of Haematological Cancer Odense (CITCO), Department of Clinical Immunology, Odense University Hospital, University of Southern Denmark, 5000 Odense C, Denmark
| | - Lisa N Kiani
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK
| | - Uroosa Chughtai
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK
| | - Rachel Heon-Roberts
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Maria Claudia Caiazza
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - William McGuinness
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Ricardo Márquez-Gómez
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Jane Vowles
- James Martin Stem Cell Facility, Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK
| | - Fiona S Bunn
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK
| | - Janine Brandes
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK
| | - Peter Kilfeather
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Jack P Connor
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Hugo J R Fernandes
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK
| | - Tara M Caffrey
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK
| | - Morten Meyer
- Department of Neurobiology Research, Institute of Molecular Medicine, University of Southern Denmark, 5000 Odense C, Denmark; Department of Neurology, Odense University Hospital, 5000 Odense C, Denmark
| | - Sally A Cowley
- James Martin Stem Cell Facility, Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK
| | - Martin R Larsen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark
| | - Richard Wade-Martins
- Oxford Parkinson's Disease Centre, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK; Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, UK.
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26
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Xu M, Yang A, Xia J, Jiang J, Liu CF, Ye Z, Ma J, Yang S. Protein glycosylation in urine as a biomarker of diseases. Transl Res 2023; 253:95-107. [PMID: 35952983 DOI: 10.1016/j.trsl.2022.08.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 02/01/2023]
Abstract
Human body fluids have become an indispensable resource for clinical research, diagnosis and prognosis. Urine is widely used to discover disease-specific glycoprotein biomarkers because of its recurrently non-invasive collection and disease-indicating properties. While urine is an unstable fluid in that its composition changes with ingested nutrients and further as it is excreted through micturition, urinary proteins are more stable and their abnormal glycosylation is associated with diseases. It is known that aberrant glycosylation can define tumor malignancy and indicate disease initiation and progression. However, a thorough and translational survey of urinary glycosylation in diseases has not been performed. In this article, we evaluate the clinical applications of urine, introduce methods for urine glycosylation analysis, and discuss urine glycoprotein biomarkers. We emphasize the importance of mining urinary glycoproteins and searching for disease-specific glycosylation in various diseases (including cancer, neurodegenerative diseases, diabetes, and viral infections). With advances in mass spectrometry-based glycomics/glycoproteomics/glycopeptidomics, characterization of disease-specific glycosylation will optimistically lead to the discovery of disease-related urinary biomarkers with better sensitivity and specificity in the near future.
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Affiliation(s)
- Mingming Xu
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Arthur Yang
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Jun Xia
- Clinical Laboratory Center, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China
| | - Junhong Jiang
- Department of Pulmonary and Critical Care Medicine, Dushu Lake Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Chun-Feng Liu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhenyu Ye
- Department of General Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Junfeng Ma
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Georgetown University, Washington, District of Columbia.
| | - Shuang Yang
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China.
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27
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Sun F, Suttapitugsakul S, Wu R. Systematic characterization of extracellular glycoproteins using mass spectrometry. MASS SPECTROMETRY REVIEWS 2023; 42:519-545. [PMID: 34047389 PMCID: PMC8627532 DOI: 10.1002/mas.21708] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 05/10/2021] [Accepted: 05/13/2021] [Indexed: 05/13/2023]
Abstract
Surface and secreted glycoproteins are essential to cells and regulate many extracellular events. Because of the diversity of glycans, the low abundance of many glycoproteins, and the complexity of biological samples, a system-wide investigation of extracellular glycoproteins is a daunting task. With the development of modern mass spectrometry (MS)-based proteomics, comprehensive analysis of different protein modifications including glycosylation has advanced dramatically. This review focuses on the investigation of extracellular glycoproteins using MS-based proteomics. We first discuss the methods for selectively enriching surface glycoproteins and investigating protein interactions on the cell surface, followed by the application of MS-based proteomics for surface glycoprotein dynamics analysis and biomarker discovery. We then summarize the methods to comprehensively study secreted glycoproteins by integrating various enrichment approaches with MS-based proteomics and their applications for global analysis of secreted glycoproteins in different biological samples. Collectively, MS significantly expands our knowledge of extracellular glycoproteins and enables us to identify extracellular glycoproteins as potential biomarkers for disease detection and drug targets for disease treatment.
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Affiliation(s)
| | | | - Ronghu Wu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
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28
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Li S, Zhu J, Lubman DM, Zhou H, Tang H. GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching. J Proteome Res 2023; 22:1501-1509. [PMID: 36802412 PMCID: PMC10164058 DOI: 10.1021/acs.jproteome.3c00066] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Liquid chromatography coupled with tandem mass spectrometry is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g., the commercial software Byonic) analyzes the individual data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.
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Affiliation(s)
- Sujun Li
- Department of Blood Transfusion, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China.,JiangXi Key Laboratory of Transfusion Medicine, Nanchang 330000, China.,Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
| | - Jianhui Zhu
- Department of Surgery, University of Michigan, Medical Center, Ann Arbor, Michigan 48109, United States
| | - David M Lubman
- Department of Surgery, University of Michigan, Medical Center, Ann Arbor, Michigan 48109, United States
| | - He Zhou
- Shenzhen Dengding Biopharma Co. Ltd., Shenzhen 518000, China
| | - Haixu Tang
- Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
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29
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Gao Z, Chen S, Du J, Wu Z, Ge W, Gao S, Zhou Z, Yang X, Xing Y, Shi M, Hu Y, Tang W, Xia J, Zhang X, Jiang J, Yang S. Quantitative analysis of fucosylated glycoproteins by immobilized lectin-affinity fluorescent labeling. RSC Adv 2023; 13:6676-6687. [PMID: 36860533 PMCID: PMC9969232 DOI: 10.1039/d3ra00072a] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/14/2023] [Indexed: 03/02/2023] Open
Abstract
Human biofluids are often used to discover disease-specific glycosylation, since abnormal changes in protein glycosylation can discern physiopathological states. Highly glycosylated proteins in biofluids make it possible to identify disease signatures. Glycoproteomic studies on saliva glycoproteins showed that fucosylation was significantly increased during tumorigenesis and that glycoproteins became hyperfucosylated in lung metastases, and tumor stage is associated with fucosylation. Quantification of salivary fucosylation can be achieved by mass spectrometric analysis of fucosylated glycoproteins or fucosylated glycans; however, the use of mass spectrometry is non-trivial for clinical practice. Here, we developed a high-throughput quantitative method, lectin-affinity fluorescent labeling quantification (LAFLQ), to quantify fucosylated glycoproteins without relying on mass spectrometry. Lectins with a specific affinity for fucoses are immobilized on the resin and effectively capture fluorescently labeled fucosylated glycoproteins, which are further quantitatively characterized by fluorescence detection in a 96-well plate. Our results demonstrated that serum IgG can be accurately quantified by lectin and fluorescence detection. Quantification in saliva showed significantly higher fucosylation in lung cancer patients compared to healthy controls or other non-cancer diseases, suggesting that this method has the potential to quantify stage-related fucosylation in lung cancer saliva.
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Affiliation(s)
- Ziyuan Gao
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University Suzhou 215006 China
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University Suzhou 215123 China
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Soochow University Suzhou 215006 China
| | - Sufeng Chen
- Clinical Laboratory Center, Zhejiang Provincial People's Hospital Hangzhou Zhejiang 310014 China
| | - Jing Du
- Clinical Laboratory Center, Zhejiang Provincial People's Hospital Hangzhou Zhejiang 310014 China
| | - Zhen Wu
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University Shanghai 200438 China
| | - Wei Ge
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University Suzhou 215123 China
- Department of Gastroenterology, The Second Affiliated Hospital of Soochow University Suzhou 215004 China
| | - Song Gao
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, Jiangsu Key Laboratory of Marine Biological Resources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, School of Pharmacy, Jiangsu Ocean University Lianyungang 222005 China
| | - Zeyang Zhou
- Department of General Surgery, The Second Affiliated Hospital of Soochow University Suzhou 215004 China
| | - Xiaodong Yang
- Department of General Surgery, The Second Affiliated Hospital of Soochow University Suzhou 215004 China
| | - Yufei Xing
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Soochow University Suzhou 215004 China
| | - Minhua Shi
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Soochow University Suzhou 215004 China
| | - Yunyun Hu
- Department of Gastroenterology, The Second Affiliated Hospital of Soochow University Suzhou 215004 China
| | - Wen Tang
- Department of Gastroenterology, The Second Affiliated Hospital of Soochow University Suzhou 215004 China
| | - Jun Xia
- Clinical Laboratory Center, Zhejiang Provincial People's Hospital Hangzhou Zhejiang 310014 China
| | - Xumin Zhang
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University Shanghai 200438 China
| | - Junhong Jiang
- Department of Respiratory Medicine, The First Affiliated Hospital of Soochow University Suzhou 215006 China
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Soochow University Suzhou 215006 China
| | - Shuang Yang
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University Suzhou 215123 China
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30
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Lectin-Based Affinity Enrichment and Characterization of N-Glycoproteins from Human Tear Film by Mass Spectrometry. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28020648. [PMID: 36677706 PMCID: PMC9864693 DOI: 10.3390/molecules28020648] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 01/11/2023]
Abstract
The glycosylation of proteins is one of the most common post-translational modifications (PTMs) and plays important regulatory functions in diverse biological processes such as protein stability or cell signaling. Accordingly, glycoproteins are also a consistent part of the human tear film proteome, maintaining the proper function of the ocular surface and forming the first defense barrier of the ocular immune system. Irregularities in the glycoproteomic composition of tear film might promote the development of chronic eye diseases, indicating glycoproteins as a valuable source for biomarker discovery or drug target identification. Therefore, the present study aimed to develop a lectin-based affinity method for the enrichment and concentration of tear glycoproteins/glycopeptides and to characterize their specific N-glycosylation sites by high-resolution mass spectrometry (MS). For method development and evaluation, we first accumulated native glycoproteins from human tear sample pools and assessed the enrichment efficiency of different lectin column systems by 1D gel electrophoresis and specific protein stainings (Coomassie and glycoproteins). The best-performing multi-lectin column system (comprising the four lectins ConA, JAC, WGA, and UEA I, termed 4L) was applied to glycopeptide enrichment from human tear sample digests, followed by MS-based detection and localization of their specific N-glycosylation sites. As the main result, our study identified a total of 26 N glycosylation sites of 11 N-glycoproteins in the tear sample pools of healthy individuals (n = 3 biological sample pools). Amongst others, we identified tear film proteins lactotransferrin (N497 and N642, LTF), Ig heavy chain constant α-1 (N144 and 340, IGHA1), prolactin-inducible protein (N105, PIP), and extracellular lacritin (N105, LACRT) as highly reliable and significant N glycoproteins, already associated with the pathogenesis of various chronic eye diseases such as dry eye syndrome (DES). In conclusion, the results of the present study will serve as an important tear film N-glycoprotein catalog for future studies focusing on human tear film and ocular surface-related inflammatory diseases.
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Abstract
O-Glycoproteases are an emerging class of enzymes that selectively digest glycoproteins at positions decorated with specific O-linked glycans. O-Glycoprotease substrates range from any O-glycoprotein (albeit with specific O-glycan modifications) to only glycoproteins harboring specific O-glycosylated sequence motifs, such as those found in mucin domains. Their utility for multiple glycoproteomic applications is driving the search to both discover new O-glycoproteases and to understand how structural features of characterized O-glycoproteases influence their substrate specificities. One challenge of defining O-glycoprotease specificity restraints is the need to characterize O-glycopeptides with site-specific analysis of O-glycosites. Here, we demonstrate how O-Pair Search, a recently developed O-glycopeptide-centric identification platform that enables rapid searches and confident O-glycosite localization, can be used to determine substrate specificities of various O-glycoproteases de novo from LC-MS/MS data of O-glycopeptides. Using secreted protease of C1 esterase inhibitor (StcE) from enterohemorrhagic Escherichia coli and O-endoprotease OgpA from Akkermansia mucinophila, we explore numerous settings that effect O-glycopeptide identification and show how non-specific and semi-tryptic searches of O-glycopeptide data can produce candidate cleavage motifs. These putative motifs can be further used to define new protease cleavage settings that lower search times and improve O-glycopeptide identifications. We use this platform to generate a consensus motif for the recently characterized immunomodulating metalloprotease (IMPa) from Pseudomonas aeruginosa and show that IMPa is a favorable O-glycoprotease for characterizing densely O-glycosylated mucin-domain glycoproteins.
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Affiliation(s)
- Nicholas M Riley
- Department of Chemistry, Sarafan ChEM-H, Stanford University, Stanford, California, USA.
| | - Carolyn R Bertozzi
- Department of Chemistry, Sarafan ChEM-H, Stanford University, Stanford, California, USA.
- Howard Hughes Medical Institute, Stanford, California, USA
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32
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Abstract
Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.
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Affiliation(s)
- Daniel Bojar
- Department
of Chemistry and Molecular Biology, University
of Gothenburg, Gothenburg 41390, Sweden
- Wallenberg
Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg 41390, Sweden
| | - Frederique Lisacek
- Proteome
Informatics Group, Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer
Science Department & Section of Biology, University of Geneva, route de Drize 7, CH-1227, Geneva, Switzerland
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33
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Yu K, Wang Y, Zheng Y, Liu Z, Zhang Q, Wang S, Zhao Q, Zhang X, Li X, Xu RH, Liu ZX. qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast. Nucleic Acids Res 2022; 51:D479-D487. [PMID: 36165955 PMCID: PMC9825568 DOI: 10.1093/nar/gkac820] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/26/2022] [Accepted: 09/14/2022] [Indexed: 01/29/2023] Open
Abstract
Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.
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Affiliation(s)
| | | | | | | | - Qingfeng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Siyu Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xiaolong Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xiaoxing Li
- Precision Medicine Institute, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Rui-Hua Xu
- Correspondence may also be addressed to Rui-Hua Xu. Tel: +86 20 8734 3228; Fax: +86 20 8734 3392;
| | - Ze-Xian Liu
- To whom correspondence should be addressed. Tel: +86 20 8734 2025; Fax: +86 20 8734 2522;
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34
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Mechref Y, Peng W, Gautam S, Ahmadi P, Lin Y, Zhu J, Zhang J, Liu S, Singal AG, Parikh ND, Lubman DM. Mass spectrometry based biomarkers for early detection of HCC using a glycoproteomic approach. Adv Cancer Res 2022; 157:23-56. [PMID: 36725111 PMCID: PMC10014290 DOI: 10.1016/bs.acr.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide and 80%-90% of HCC develops in patients that have underlying cirrhosis. Better methods of surveillance are needed to increase early detection of HCC and the proportion of patients that can be offered curative therapies. Recent work in novel mass spec-based methods for glycomic and glycopeptide analysis for discovery and confirmation of markers for early detection of HCC versus cirrhosis is reviewed in this chapter. Results from recent work in these fields by several groups and the progress made in developing markers of early HCC which can outperform the current serum-based markers are described and discussed. Also, recent developments in isoform analysis of glycans and glycopeptides and in various mass spec fragmentation methods will be described and discussed.
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Affiliation(s)
- Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States.
| | - Wenjing Peng
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Sakshi Gautam
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Parisa Ahmadi
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Yu Lin
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - Jianhui Zhu
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - Jie Zhang
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - Suyu Liu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Amit G Singal
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - David M Lubman
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States.
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35
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Ren W, Bian Q, Cai Y. Mass spectrometry-based N-glycosylation analysis in kidney disease. Front Mol Biosci 2022; 9:976298. [PMID: 36072428 PMCID: PMC9442644 DOI: 10.3389/fmolb.2022.976298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/18/2022] [Indexed: 11/14/2022] Open
Abstract
Kidney disease is a global health concern with an enormous expense. It is estimated that more than 10% of the population worldwide is affected by kidney disease and millions of patients would progress to death prematurely and unnecessarily. Although creatinine detection and renal biopsy are well-established tools for kidney disease diagnosis, they are limited by several inevitable defects. Therefore, diagnostic tools need to be upgraded, especially for the early stage of the disease and possible progression. As one of the most common post-translational modifications of proteins, N-glycosylation plays a vital role in renal structure and function. Deepening research on N-glycosylation in kidney disease provides new insights into the pathophysiology and paves the way for clinical application. In this study, we reviewed recent N-glycosylation studies on several kidney diseases. We also summarized the development of mass spectrometric methods in the field of N-glycoproteomics and N-glycomics.
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Affiliation(s)
- Weifu Ren
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Nephrology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Qi Bian
- Department of Nephrology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yan Cai
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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36
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Ciesla J, Moreno I, Munger J. TNFα-induced metabolic reprogramming drives an intrinsic anti-viral state. PLoS Pathog 2022; 18:e1010722. [PMID: 35834576 PMCID: PMC9321404 DOI: 10.1371/journal.ppat.1010722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 07/26/2022] [Accepted: 07/01/2022] [Indexed: 11/22/2022] Open
Abstract
Cytokines induce an anti-viral state, yet many of the functional determinants responsible for limiting viral infection are poorly understood. Here, we find that TNFα induces significant metabolic remodeling that is critical for its anti-viral activity. Our data demonstrate that TNFα activates glycolysis through the induction of hexokinase 2 (HK2), the isoform predominantly expressed in muscle. Further, we show that glycolysis is broadly important for TNFα-mediated anti-viral defense, as its inhibition attenuates TNFα’s ability to limit the replication of evolutionarily divergent viruses. TNFα was also found to modulate the metabolism of UDP-sugars, which are essential precursor substrates for glycosylation. Our data indicate that TNFα increases the concentration of UDP-glucose, as well as the glucose-derived labeling of UDP-glucose and UDP-N-acetyl-glucosamine in a glycolytically-dependent manner. Glycolysis was also necessary for the TNFα-mediated accumulation of several glycosylated anti-viral proteins. Consistent with the importance of glucose-driven glycosylation, glycosyl-transferase inhibition attenuated TNFα’s ability to promote the anti-viral cell state. Collectively, our data indicate that cytokine-mediated metabolic remodeling is an essential component of the anti-viral response. Viral infection often activates a host cell’s intrinsic immune response resulting in the cellular secretion of cytokines, important host-defense molecules. These cytokines act on neighboring cells to make them less permissive to viral infection. Many of the mechanisms through which cytokines promote a less permissive cell state remain unclear. Our data indicate that treatment with the anti-viral cytokine TNFα induces substantial changes to cellular metabolic activity, including activating glucose metabolism. We find that these TNFα-induced metabolic changes are critical for TNFα to limit the replication of diverse viruses including Human Cytomegalovirus and two Coronaviruses, OC43 and SARS-CoV-2. Inhibition of glucose metabolism during TNFα treatment prevented the expression of a variety of known cellular anti-viral proteins. Collectively, our data indicate that cytokine-induced metabolic remodeling is an important component of TNFα’s ability to promote a less permissive cell state and raises further questions about the mechanisms through which specific cytokine-induced metabolic activities contribute to various aspects of anti-viral defense.
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Affiliation(s)
- Jessica Ciesla
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Isreal Moreno
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Joshua Munger
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
- * E-mail:
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37
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Xu M, Jin H, Wu Z, Han Y, Chen J, Mao C, Hao P, Zhang X, Liu CF, Yang S. Mass Spectrometry-Based Analysis of Serum N-Glycosylation Changes in Patients with Parkinson's Disease. ACS Chem Neurosci 2022; 13:1719-1726. [PMID: 35640092 DOI: 10.1021/acschemneuro.2c00264] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
It is urgently needed to find reliable biofluid biomarkers for early diagnosis of Parkinson's disease in order to achieve better treatment. Promising biomarkers can be found in Parkinson's disease-related glycoproteins as aberrant protein glycosylation plays an important role in disease progression. However, current information on serum N-glycoproteomic changes in Parkinson's disease is still limited. Here, we used glycoproteomics methods, which combine the solid-phase chemoenzymatic method, lectin affinity chromatography, and hydrophilic interaction chromatography with high-resolution mass spectrometry, to analyze the glycans, glycosites, and intact glycopeptides of serum. Increased abundance of glycans containing core fucose, sialic acid, and bisecting N-acetyl glucosamine was detected at the overall glycan level and also at specific glycosites of glycopeptides. Five Parkinson's disease-associated proteins with this type of N-glycosylation changes were also identified. We propose that the revealed site-specific N-glycosylation changes in serum can be potential biomarkers for Parkinson's disease.
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Affiliation(s)
- Mingming Xu
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, China
| | - Hong Jin
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Zhen Wu
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Ying Han
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jing Chen
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Piliang Hao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xumin Zhang
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Chun-Feng Liu
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.,Jiangsu Key Laboratory of Neuropsychiatric Diseases and Institute of Neuroscience, Soochow University, Suzhou 215123, China
| | - Shuang Yang
- Center for Clinical Mass Spectrometry, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, China
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38
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Fan X, Song Q, Sun DE, Hao Y, Wang J, Wang C, Chen X. Cell-type-specific labeling and profiling of glycans in living mice. Nat Chem Biol 2022; 18:625-633. [PMID: 35513511 DOI: 10.1038/s41589-022-01016-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 03/15/2022] [Indexed: 11/09/2022]
Abstract
Metabolic labeling of glycans with clickable unnatural sugars has enabled glycan analysis in multicellular systems. However, cell-type-specific labeling of glycans in vivo remains challenging. Here we develop genetically encoded metabolic glycan labeling (GeMGL), a cell-type-specific strategy based on a bump-and-hole pair of an unnatural sugar and its matching engineered enzyme. N-pentynylacetylglucosamine (GlcNAl) serves as a bumped analog of N-acetylglucosamine (GlcNAc) that is specifically incorporated into glycans of cells expressing a UDP-GlcNAc pyrophosphorylase mutant, AGX2F383G. GeMGL with the 1,3-di-O-propionylated GlcNAl (1,3-Pr2GlcNAl) and AGX2F383G pair was demonstrated in cell cocultures, and used for specific labeling of glycans in mouse xenograft tumors. By generating a transgenic mouse line with AGX2F383G expressed under a cardiomyocyte-specific promoter, we performed specific imaging of cardiomyocyte glycans in the heart and identified 582 cardiomyocyte O-GlcNAcylated proteins with no interference from other cardiac cell types. GeMGL will facilitate cell-type-specific glycan imaging and glycoproteomics in various tissues and disease models.
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Affiliation(s)
- Xinqi Fan
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Qitao Song
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - De-En Sun
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Yi Hao
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Jingyang Wang
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Chunting Wang
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Xing Chen
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China. .,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China. .,Synthetic and Functional Biomolecules Center, Peking University, Beijing, China. .,Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China.
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39
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Pujić I, Perreault H. Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins. MASS SPECTROMETRY REVIEWS 2022; 41:488-507. [PMID: 33393161 DOI: 10.1002/mas.21679] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.
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Affiliation(s)
- Ivona Pujić
- Chemistry Department, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Hélène Perreault
- Chemistry Department, University of Manitoba, Winnipeg, Manitoba, Canada
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40
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Wang N, Zhang X, Li X, Liu C, Yang M, Han B, Hai C, Su G, Li G, Zhao Y. Cysteine is highly enriched in the canonical N-linked glycosylation motif of bovine spermatozoa N-Glycoproteome. Theriogenology 2022; 184:1-12. [DOI: 10.1016/j.theriogenology.2022.02.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 02/16/2022] [Accepted: 02/16/2022] [Indexed: 12/15/2022]
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41
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Xin M, You S, Xu Y, Shi W, Zhu B, Shen J, Wu J, Li C, Chen Z, Su Y, Shi J, Sun S. Precision glycoproteomics reveals distinctive N-glycosylation in human spermatozoa. Mol Cell Proteomics 2022; 21:100214. [PMID: 35183770 PMCID: PMC8958358 DOI: 10.1016/j.mcpro.2022.100214] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/22/2022] [Accepted: 02/06/2022] [Indexed: 12/21/2022] Open
Abstract
Spermatozoon represents a very special cell type in human body, and glycosylation plays essential roles in its whole life including spermatogenesis, maturation, capacitation, sperm–egg recognition, and fertilization. In this study, by mapping the most comprehensive N-glycoproteome of human spermatozoa using our recently developed site-specific glycoproteomic approaches, we show that spermatozoa contain a number of distinctive glycoproteins, which are mainly involved in spermatogenesis, acrosome reaction and sperm:oocyte membrane binding, and fertilization. Heavy fucosylation is observed on 14 glycoproteins mostly located at extracellular and cell surface regions in spermatozoa but not in other tissues. Sialylation and Lewis epitopes are enriched in the biological process of immune response in spermatozoa, while bisected core structures and LacdiNAc structures are highly expressed in acrosome. These data deepen our knowledge about glycosylation in spermatozoa and lay the foundation for functional study of glycosylation and glycan structures in male infertility. A precision site-specific glycoproteome is documented in human spermatozoa. Distinctive glycoproteins and heavy fucosylation are detected in spermatozoa. Sialylation and Lewis epitopes are related to immune response of spermatozoa. Bisected core structures and LacdiNAc are enriched on acrosome of spermatozoa.
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42
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Song W, Isaji T, Nakano M, Liang C, Fukuda T, Gu J. O-GlcNAcylation regulates β1,4-GlcNAc-branched N-glycan biosynthesis via the OGT/SLC35A3/GnT-IV axis. FASEB J 2022; 36:e22149. [PMID: 34981577 DOI: 10.1096/fj.202101520r] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/14/2021] [Accepted: 12/22/2021] [Indexed: 12/21/2022]
Abstract
N-Linked glycosylation and O-linked N-acetylglucosamine (O-GlcNAc) are important protein post-translational modifications that are orchestrated by a diverse set of gene products. Thus far, the relationship between these two types of glycosylation has remained elusive, and it is unclear whether one influences the other via UDP-GlcNAc, which is a common donor substrate. Theoretically, a decrease in O-GlcNAcylation may increase the products of GlcNAc-branched N-glycans. In this study, via examination by lectin blotting, HPLC, and mass spectrometry analysis, however, we found that the amounts of GlcNAc-branched tri-antennary N-glycans catalyzed by N-acetylglucosaminyltransferase IV (GnT-IV) and tetra-antennary N-glycans were significantly decreased in O-GlcNAc transferase knockdown cells (OGT-KD) compared with those in wild type cells. We examined this specific alteration by focusing on SLC35A3, which is the main UDP-GlcNAc transporter in mammals that is believed to modulate GnT-IV activation. It is interesting that a deficiency of SLC35A3 specifically leads to a decrease in the amounts of GlcNAc-branched tri- and tetra-antennary N-glycans. Furthermore, co-immunoprecipitation experiments have shown that SLC35A3 interacts with GnT-IV, but not with N-acetylglucosaminyltransferase V. Western blot and chemoenzymatic labeling assay have confirmed that OGT modifies SLC35A3 and that O-GlcNAcylation contributes to its stability. Furthermore, we found that SLC35A3-KO enhances cell spreading and suppresses both cell migration and cell proliferation, which is similar to the phenomena observed in the OGT-KD cells. Taken together, these data are the first to demonstrate that O-GlcNAcylation specifically governs the biosynthesis of tri- and tetra-antennary N-glycans via the OGT-SLC35A3-GnT-IV axis.
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Affiliation(s)
- Wanli Song
- Division of Regulatory Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Tomoya Isaji
- Division of Regulatory Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Miyako Nakano
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-hiroshima, Japan
| | - Caixia Liang
- Division of Regulatory Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Tomohiko Fukuda
- Division of Regulatory Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Jianguo Gu
- Division of Regulatory Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, Sendai, Japan
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OUP accepted manuscript. Glycobiology 2022; 32:646-650. [DOI: 10.1093/glycob/cwac025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/06/2022] [Indexed: 11/14/2022] Open
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Zhang H, Ao M, Boja A, Schnaubelt M, Hu Y. OmicsOne: associate omics data with phenotypes in one-click. Clin Proteomics 2021; 18:29. [PMID: 34895137 PMCID: PMC8903648 DOI: 10.1186/s12014-021-09334-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/22/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The rapid advancements of high throughput "omics" technologies have brought a massive amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset and the discovery of interesting genes, proteins, lipids, glycans, metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed for data analysis and visualization. However, it still lacks an efficient way to investigate the phenotypes with multiple omics data. Here, we present OmicsOne as an interactive web-based framework for rapid phenotype association analysis of multi-omic data by integrating quality control, statistical analysis, and interactive data visualization on 'one-click'. MATERIALS AND METHODS OmicsOne was applied on the previously published proteomic and glycoproteomic data sets of high-grade serous ovarian carcinoma (HGSOC) and the published proteome data set of lung squamous cell carcinoma (LSCC) to confirm its performance. The data was analyzed through six main functional modules implemented in OmicsOne: (1) phenotype profiling, (2) data preprocessing and quality control, (3) knowledge annotation, (4) phenotype associated features discovery, (5) correlation and regression model analysis for phenotype association analysis on individual features, and (6) enrichment analysis for phenotype association analysis on interested feature sets. RESULTS We developed an integrated software solution, OmicsOne, for the phenotype association analysis on multi-omics data sets. The application of OmicsOne on the public data set of ovarian cancer data showed that the software could confirm the previous observations consistently and discover new evidence for HNRNPU and a glycopeptide of HYOU1 as potential biomarkers for HGSOC data sets. The performance of OmicsOne was further demonstrated in the Tumor and NAT comparison study on the proteome data set of LSCC. CONCLUSIONS OmicsOne can effectively simplify data analysis and reveal the significant associations between phenotypes and potential biomarkers, including genes, proteins, and glycopeptides, in minutes to assist users to understand aberrant biological processes.
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Affiliation(s)
- Hui Zhang
- School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Minghui Ao
- School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Arianna Boja
- Mount Hebron High School, Ellicott City, MD, 21042, USA
| | | | - Yingwei Hu
- School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA.
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Pakhrin SC, Aoki-Kinoshita KF, Caragea D, KC DB. DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction. Molecules 2021; 26:molecules26237314. [PMID: 34885895 PMCID: PMC8658957 DOI: 10.3390/molecules26237314] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 11/22/2021] [Accepted: 11/26/2021] [Indexed: 12/21/2022] Open
Abstract
Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.
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Affiliation(s)
- Subash C. Pakhrin
- School of Computing, Wichita State University, 1845 Fairmount St., Wichita, KS 67260, USA;
| | | | - Doina Caragea
- Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA;
| | - Dukka B. KC
- Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA
- Correspondence: ; Tel.: +1-906-487-1657
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Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJH, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 2021; 184:5031-5052.e26. [PMID: 34534465 PMCID: PMC8654574 DOI: 10.1016/j.cell.2021.08.023] [Citation(s) in RCA: 241] [Impact Index Per Article: 80.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/19/2021] [Accepted: 08/18/2021] [Indexed: 02/07/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.
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Affiliation(s)
- Liwei Cao
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Chen Huang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - T Mamie Lih
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David J Clark
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | | | | | - Weiming Yang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jianbo Pan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Rita Jui-Hsien Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ruiyang Liu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Houxiang Zhu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Peter Ronning
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Hariharan Easwaran
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ludmila Danilova
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Seungyeul Yoo
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Scott D Jewell
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | | | | | - Qing Kay Li
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael H Roehrl
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Pei Wang
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | | | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Oliver F Bathe
- Departments of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA; The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.
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The structure of an archaeal oligosaccharyltransferase provides insight into the strict exclusion of proline from the N-glycosylation sequon. Commun Biol 2021; 4:941. [PMID: 34354228 PMCID: PMC8342417 DOI: 10.1038/s42003-021-02473-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
Oligosaccharyltransferase (OST) catalyzes oligosaccharide transfer to the Asn residue in the N-glycosylation sequon, Asn-X-Ser/Thr, where Pro is strictly excluded at position X. Considering the unique structural properties of proline, this exclusion may not be surprising, but the structural basis for the rejection of Pro residues should be explained explicitly. Here we determined the crystal structure of an archaeal OST in a complex with a sequon-containing peptide and dolichol-phosphate to a 2.7 Å resolution. The sequon part in the peptide forms two inter-chain hydrogen bonds with a conserved amino acid motif, TIXE. We confirmed the essential role of the TIXE motif and the adjacent regions by extensive alanine-scanning of the external loop 5. A Ramachandran plot revealed that the ring structure of the Pro side chain is incompatible with the ϕ backbone dihedral angle around -150° in the rigid sequon-TIXE structure. The present structure clearly provides the structural basis for the exclusion of Pro residues from the N-glycosylation sequon.
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48
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Munteanu CVA, Chirițoiu GN, Chirițoiu M, Ghenea S, Petrescu AJ, Petrescu ȘM. Affinity proteomics and deglycoproteomics uncover novel EDEM2 endogenous substrates and an integrative ERAD network. Mol Cell Proteomics 2021; 20:100125. [PMID: 34332121 PMCID: PMC8455867 DOI: 10.1016/j.mcpro.2021.100125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 07/09/2021] [Accepted: 07/25/2021] [Indexed: 02/08/2023] Open
Abstract
Various pathologies result from disruptions to or stress of endoplasmic reticulum (ER) homeostasis, such as Parkinson's disease and most neurodegenerative illnesses, diabetes, pulmonary fibrosis, viral infections and cancers. A critical process in maintaining ER homeostasis is the selection of misfolded proteins by the ER quality-control system (ERQC) for destruction via ER-associated degradation (ERAD). One key protein proposed to act during the first steps of misfolded glycoprotein degradation is the ER degradation-enhancing α-mannosidase-like protein 2 (EDEM2). Therefore, characterization of the EDEM2 associated proteome is of great interest. We took advantage of using melanoma cells overexpressing EDEM2 as a cancer model system, to start documenting at the deglycoproteome level (N-glycosites identification) the emerging link between ER homeostasis and cancer progression. The dataset created for identifying the EDEM2 glyco-clients carrying high mannose/hybrid N-glycans provides a comprehensive N-glycosites analysis mapping over 1000 N-glycosites on more than 600 melanoma glycoproteins. To identify EDEM2-associated proteins we used affinity-proteomics and proteome-wide analysis of sucrose density fractionation in an integrative workflow. Using intensity and spectral count-based quantification, we identify seven new EDEM2 partners, all of which are involved in ERQC and ERAD. Moreover, we defined novel endogenous candidates for EDEM2-dependent ERAD by combining deglycoproteomics, SILAC-based proteomics, and biochemical methods. These included tumor antigens and several ER-transiting endogenous melanoma proteins, including ITGA1 and PCDH2, the expression of which was negatively correlated with that of EDEM2. Tumor antigens are key in the antigen presentation process, whilst ITGA1 and PCDH2 are involved in melanoma metastasis and invasion. EDEM2 could therefore have a regulatory role in melanoma through the modulation of these glycoproteins degradation and trafficking. The data presented herein suggest that EDEM2 is involved in ER homeostasis to a greater extent than previously suggested.
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Affiliation(s)
- Cristian V A Munteanu
- Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry, Splaiul Independenței 296, 060031, Bucharest, Romania
| | - Gabriela N Chirițoiu
- Department of Molecular Cell Biology, Institute of Biochemistry, Splaiul Independenței 296, 060031, Bucharest, Romania
| | - Marioara Chirițoiu
- Department of Molecular Cell Biology, Institute of Biochemistry, Splaiul Independenței 296, 060031, Bucharest, Romania
| | - Simona Ghenea
- Department of Molecular Cell Biology, Institute of Biochemistry, Splaiul Independenței 296, 060031, Bucharest, Romania
| | - Andrei-Jose Petrescu
- Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry, Splaiul Independenței 296, 060031, Bucharest, Romania
| | - Ștefana M Petrescu
- Department of Molecular Cell Biology, Institute of Biochemistry, Splaiul Independenței 296, 060031, Bucharest, Romania.
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Michalak M, Kalteis MS, Ahadova A, Kloor M, Kriegsmann M, Kriegsmann K, Warnken U, Helm D, Kopitz J. Differential Glycosite Profiling-A Versatile Method to Compare Membrane Glycoproteomes. Molecules 2021; 26:molecules26123564. [PMID: 34200965 PMCID: PMC8230608 DOI: 10.3390/molecules26123564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/03/2021] [Accepted: 06/07/2021] [Indexed: 11/16/2022] Open
Abstract
Glycosylation is the most prevalent and varied form of post-translational protein modifications. Protein glycosylation regulates multiple cellular functions, including protein folding, cell adhesion, molecular trafficking and clearance, receptor activation, signal transduction, and endocytosis. In particular, membrane proteins are frequently highly glycosylated, which is both linked to physiological processes and of high relevance in various disease mechanisms. The cellular glycome is increasingly considered to be a therapeutic target. Here we describe a new strategy to compare membrane glycoproteomes, thereby identifying proteins with altered glycan structures and the respective glycosites. The workflow started with an optimized procedure for the digestion of membrane proteins followed by the lectin-based isolation of glycopeptides. Since alterations in the glycan part of a glycopeptide cause mass alterations, analytical size exclusion chromatography was applied to detect these mass shifts. N-glycosidase treatment combined with nanoUPLC-coupled mass spectrometry identified the altered glycoproteins and respective glycosites. The methodology was established using the colon cancer cell line CX1, which was treated with 2-deoxy-glucose-a modulator of N-glycosylation. The described methodology is not restricted to cell culture, as it can also be adapted to tissue samples or body fluids. Altogether, it is a useful module in various experimental settings that target glycan functions.
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Affiliation(s)
- Malwina Michalak
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany; (M.S.K.); (A.A.); (M.K.)
- Clinical Cooperation Unit Applied Tumor Biology, DKFZ (German Cancer Research Center) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Correspondence: (M.M.); (J.K.); Tel.: +49-6221-56-6167 (M.M.)
| | - Martin Simon Kalteis
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany; (M.S.K.); (A.A.); (M.K.)
- Clinical Cooperation Unit Applied Tumor Biology, DKFZ (German Cancer Research Center) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Aysel Ahadova
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany; (M.S.K.); (A.A.); (M.K.)
- Clinical Cooperation Unit Applied Tumor Biology, DKFZ (German Cancer Research Center) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Matthias Kloor
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany; (M.S.K.); (A.A.); (M.K.)
- Clinical Cooperation Unit Applied Tumor Biology, DKFZ (German Cancer Research Center) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Mark Kriegsmann
- Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany;
| | - Katharina Kriegsmann
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany;
| | - Uwe Warnken
- Clinical Cooperation Unit Neurooncology, DKFZ (German Cancer Research Center), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
| | - Dominic Helm
- Genomics and Proteomics Core Facility, MS-based Protein Analysis Unit, DKFZ (German Cancer Research Center) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
| | - Jürgen Kopitz
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany; (M.S.K.); (A.A.); (M.K.)
- Clinical Cooperation Unit Applied Tumor Biology, DKFZ (German Cancer Research Center) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Correspondence: (M.M.); (J.K.); Tel.: +49-6221-56-6167 (M.M.)
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Wang L, Balmat TJ, Antonia AL, Constantine FJ, Henao R, Burke TW, Ingham A, McClain MT, Tsalik EL, Ko ER, Ginsburg GS, DeLong MR, Shen X, Woods CW, Hauser ER, Ko DC. An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility. Genome Med 2021; 13:83. [PMID: 34001247 PMCID: PMC8127495 DOI: 10.1186/s13073-021-00904-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 05/05/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND While genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. RESULTS Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb)) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs associated with both severe COVID-19 and other human traits demonstrated colocalization of the GWAS signal at the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN). This finding points to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity. CONCLUSIONS Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches. The iCPAGdb web portal is accessible at http://cpag.oit.duke.edu and the software code at https://github.com/tbalmat/iCPAGdb .
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Affiliation(s)
- Liuyang Wang
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA
| | - Thomas J Balmat
- Duke Research Computing, Duke University, Durham, NC, 27710, USA
| | - Alejandro L Antonia
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA
| | - Florica J Constantine
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
| | - Ricardo Henao
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
| | - Thomas W Burke
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
| | - Andy Ingham
- Duke Research Computing, Duke University, Durham, NC, 27710, USA
| | - Micah T McClain
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
- Durham Veterans Affairs Health Care System, Durham, NC, 27705, USA
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
| | - Ephraim L Tsalik
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
- Durham Veterans Affairs Health Care System, Durham, NC, 27705, USA
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
| | - Emily R Ko
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
- Department of Hospital Medicine, Duke Regional Hospital, Durham, NC, 27705, USA
| | - Geoffrey S Ginsburg
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
| | - Mark R DeLong
- Duke Research Computing, Duke University, Durham, NC, 27710, USA
| | - Xiling Shen
- Department of Biomedical Engineering, Woo Center for Big Data and Precision Health, Duke University, Durham, NC, 27710, USA
| | - Christopher W Woods
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, 27710, USA
- Durham Veterans Affairs Health Care System, Durham, NC, 27705, USA
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
| | - Elizabeth R Hauser
- Duke Molecular Physiology Institute and Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, 27710, USA
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, 27705, USA
| | - Dennis C Ko
- Department of Molecular Genetics and Microbiology, School of Medicine, Duke University, 0049 CARL Building Box 3053, 213 Research Drive, Durham, NC, 27710, USA.
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, 27710, USA.
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