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Dong A, Liu C, Hua X, Yu Y, Guo Y, Wang D, Liu X, Chen H, Wang H, Zhu L. Bioinformatic analysis of structures and encoding genes of Escherichia coli surface polysaccharides sheds light on the heterologous biosynthesis of glycans. BMC Genomics 2023; 24:168. [PMID: 37016299 PMCID: PMC10072801 DOI: 10.1186/s12864-023-09269-6] [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: 09/19/2022] [Accepted: 03/22/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Surface polysaccharides (SPs), such as lipopolysaccharide (O antigen) and capsular polysaccharide (K antigen), play a key role in the pathogenicity of Escherichia coli (E. coli). Gene cluster for polysaccharide antigen biosynthesis encodes various glycosyltransferases (GTs), which drive the process of SP synthesis and determine the serotype. RESULTS In this study, a total of 7,741 E. coli genomic sequences were chosen for systemic data mining. The monosaccharides in both O and K antigens were dominated by D-hexopyranose, and the SPs in 70-80% of the strains consisted of only the five most common hexoses (or some of them). The linkages between the two monosaccharides were mostly α-1,3 (23.15%) and β-1,3 (20.49%) bonds. Uridine diphosphate activated more than 50% of monosaccharides for glycosyltransferase reactions. These results suggest that the most common pathways could be integrated into chassis cells to promote glycan biosynthesis. We constructed a database (EcoSP, http://ecosp.dmicrobe.cn/ ) for browse this information, such as monosaccharide synthesis pathways. It can also be used for serotype analysis and GT annotation of known or novel E. coli sequences, thus facilitating the diagnosis and typing. CONCLUSIONS Summarizing and analyzing the properties of these polysaccharide antigens and GTs are of great significance for designing glycan-based vaccines and the synthetic glycobiology.
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Affiliation(s)
- Ao Dong
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, No. 20, Dongda Street, Fengtai District, Beijing, 100071, People's Republic of China
| | - Chengzhi Liu
- Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
- Hangzhou Digital-Micro Biotech Co., Ltd, Hangzhou, People's Republic of China
| | - Xiaoting Hua
- Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
- Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, People's Republic of China
- Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Yunsong Yu
- Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
- Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, People's Republic of China
- Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Yan Guo
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, No. 20, Dongda Street, Fengtai District, Beijing, 100071, People's Republic of China
| | - Dongshu Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, No. 20, Dongda Street, Fengtai District, Beijing, 100071, People's Republic of China
| | - Xiankai Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, No. 20, Dongda Street, Fengtai District, Beijing, 100071, People's Republic of China
| | - Huan Chen
- Department of Infectious Diseases, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
- Hangzhou Digital-Micro Biotech Co., Ltd, Hangzhou, People's Republic of China.
- Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.
| | - Hengliang Wang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, No. 20, Dongda Street, Fengtai District, Beijing, 100071, People's Republic of China.
| | - Li Zhu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, No. 20, Dongda Street, Fengtai District, Beijing, 100071, People's Republic of China.
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Abstract
Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.
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Affiliation(s)
- Serge Perez
- Centre de Recherche sur les Macromolecules Vegetales, University of Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble F-38041, France
| | - Olga Makshakova
- FRC Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, Kazan 420111, Russia
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Toukach PV, Shirkovskaya AI. Carbohydrate Structure Database and Other Glycan Databases as an Important Element of Glycoinformatics. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022. [DOI: 10.1134/s1068162022030190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhou C, Xu Q, He S, Ye W, Cao R, Wang P, Ling Y, Yan X, Wang Q, Zhang G. GTDB: an integrated resource for glycosyltransferase sequences and annotations. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2020:5857526. [PMID: 32542364 PMCID: PMC7296393 DOI: 10.1093/database/baaa047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 04/03/2020] [Accepted: 05/21/2020] [Indexed: 11/15/2022]
Abstract
Glycosyltransferases (GTs), a large class of carbohydrate-active enzymes, adds glycosyl moieties to various substrates to generate multiple bioactive compounds, including natural products with pharmaceutical or agrochemical values. Here, we first collected comprehensive information on GTs, including amino acid sequences, coding region sequences, available tertiary structures, protein classification families, catalytic reactions and metabolic pathways. Then, we developed sequence search and molecular docking processes for GTs, resulting in a GTs database (GTDB). In the present study, 520 179 GTs from approximately 21 647 species that involved in 394 kinds of different reactions were deposited in GTDB. GTDB has the following useful features: (i) text search is provided for retrieving the complete details of a query by combining multiple identifiers and data sources; (ii) a convenient browser allows users to browse data by different classifications and download data in batches; (iii) BLAST is offered for searching against pre-defined sequences, which can facilitate the annotation of the biological functions of query GTs; and lastly, (iv) GTdock using AutoDock Vina performs docking simulations of several GTs with the same single acceptor and displays the results based on 3Dmol.js allowing easy view of models.
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Affiliation(s)
- Chenfen Zhou
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Qingwei Xu
- College of Computer, Hubei University of Education, 129 Second Gaoxin Road, Wuhan Hi-Tech Zone, Wu Han 430205, China
| | - Sheng He
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Wei Ye
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Ruifang Cao
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Pengyu Wang
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Yunchao Ling
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Xing Yan
- CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, 300 Fenglin Road, Xuhui, Shanghai 200032, China
| | - Qingzhong Wang
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Guoqing Zhang
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
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Scherbinina SI, Toukach PV. Three-Dimensional Structures of Carbohydrates and Where to Find Them. Int J Mol Sci 2020; 21:E7702. [PMID: 33081008 PMCID: PMC7593929 DOI: 10.3390/ijms21207702] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.
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Affiliation(s)
- Sofya I. Scherbinina
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
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Databases and Bioinformatic Tools for Glycobiology and Glycoproteomics. Int J Mol Sci 2020; 21:ijms21186727. [PMID: 32937895 PMCID: PMC7556027 DOI: 10.3390/ijms21186727] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/03/2020] [Accepted: 09/11/2020] [Indexed: 02/07/2023] Open
Abstract
Glycosylation plays critical roles in various biological processes and is closely related to diseases. Deciphering the glycocode in diverse cells and tissues offers opportunities to develop new disease biomarkers and more effective recombinant therapeutics. In the past few decades, with the development of glycobiology, glycomics, and glycoproteomics technologies, a large amount of glycoscience data has been generated. Subsequently, a number of glycobiology databases covering glycan structure, the glycosylation sites, the protein scaffolds, and related glycogenes have been developed to store, analyze, and integrate these data. However, these databases and tools are not well known or widely used by the public, including clinicians and other researchers who are not in the field of glycobiology, but are interested in glycoproteins. In this study, the representative databases of glycan structure, glycoprotein, glycan-protein interactions, glycogenes, and the newly developed bioinformatic tools and integrated portal for glycoproteomics are reviewed. We hope this overview could assist readers in searching for information on glycoproteins of interest, and promote further clinical application of glycobiology.
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Wang X, Li C, Zhou Z, Zhang Y. Identification of Three (Iso)flavonoid Glucosyltransferases From Pueraria lobata. FRONTIERS IN PLANT SCIENCE 2019; 10:28. [PMID: 30761172 PMCID: PMC6362427 DOI: 10.3389/fpls.2019.00028] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 01/09/2019] [Indexed: 05/08/2023]
Abstract
(Iso)flavonoids are one of the largest groups of natural phenolic products conferring great value to the health of plants and humans. Pueraria lobata, a legume, has long been used in Chinese traditional medicine. (Iso)flavonoids mainly present as glycosyl-conjugates and accumulate in P. lobata roots. However, the molecular mechanism underlying the glycosylation processes in (iso)flavonoid biosynthesis are not fully understood. In the current study, three novel UDP-glycosyltransferases (PlUGT4, PlUGT15, and PlUGT57) were identified in P. lobata from RNA-seq data. Biochemical assays of these three recombinant PlUGTs showed all of them were able to glycosylate isoflavones (genistein and daidzein) at the 7-hydroxyl position in vitro. In comparison with the strict substrate specificity for PlUGT15 and PlUGT57, PlUGT4 displayed utilization of a broad range of sugar acceptors. Particularly, PlUGT15 exhibited a much higher catalytic efficiency toward isoflavones (genistein and daidzein) than any other identified 7-O-UGT from P. lobata. Moreover, the transcriptional expression patterns of these PlUGTs correlated with the accumulation of isoflavone glucosides in MeJA-treated P. lobata, suggesting their possible in vivo roles in the glycosylation process.
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Affiliation(s)
- Xin Wang
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Chinese Academy of Sciences, Wuhan, China
| | - Changfu Li
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Chinese Academy of Sciences, Wuhan, China
- Shanghai Key Laboratory of Bio-Energy Crops, Research Center for Natural Products, School of Life Sciences, Shanghai University, Shanghai, China
| | - Zilin Zhou
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Chinese Academy of Sciences, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yansheng Zhang
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Chinese Academy of Sciences, Wuhan, China
- Shanghai Key Laboratory of Bio-Energy Crops, Research Center for Natural Products, School of Life Sciences, Shanghai University, Shanghai, China
- *Correspondence: Yansheng Zhang,
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