1
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Reddy JV, Raudenbush K, Papoutsakis ET, Ierapetritou M. Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation. Biotechnol Adv 2023; 67:108179. [PMID: 37257729 DOI: 10.1016/j.biotechadv.2023.108179] [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: 11/27/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023]
Abstract
In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.
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Affiliation(s)
- Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Katherine Raudenbush
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Eleftherios Terry Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA; Delaware Biotechnology Institute, Department of Biological Sciences, University of Delaware, USA.
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA.
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2
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Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints. Nat Commun 2022; 13:2969. [PMID: 35624178 PMCID: PMC9142503 DOI: 10.1038/s41467-022-30689-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/12/2022] [Indexed: 01/20/2023] Open
Abstract
Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production. Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.
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3
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Kouka T, Akase S, Sogabe I, Jin C, Karlsson NG, Aoki-Kinoshita KF. Computational Modeling of O-Linked Glycan Biosynthesis in CHO Cells. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27061766. [PMID: 35335136 PMCID: PMC8950484 DOI: 10.3390/molecules27061766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 12/03/2022]
Abstract
Glycan biosynthesis simulation research has progressed remarkably since 1997, when the first mathematical model for N-glycan biosynthesis was proposed. An O-glycan model has also been developed to predict O-glycan biosynthesis pathways in both forward and reverse directions. In this work, we started with a set of O-glycan profiles of CHO cells transiently transfected with various combinations of glycosyltransferases. The aim was to develop a model that encapsulated all the enzymes in the CHO transfected cell lines. Due to computational power restrictions, we were forced to focus on a smaller set of glycan profiles, where we were able to propose an optimized set of kinetics parameters for each enzyme in the model. Using this optimized model we showed that the abundance of more processed glycans could be simulated compared to observed abundance, while predicting the abundance of glycans earlier in the pathway was less accurate. The data generated show that for the accurate prediction of O-linked glycosylation, additional factors need to be incorporated into the model to better reflect the experimental conditions.
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Affiliation(s)
- Thukaa Kouka
- Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo 192-8577, Japan; (S.A.); (I.S.)
- Department of Cardiology, Keio University School of Medicine, Tokyo 160-8582, Japan
- Correspondence: (T.K.); (K.F.A.-K.)
| | - Sachiko Akase
- Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo 192-8577, Japan; (S.A.); (I.S.)
| | - Isami Sogabe
- Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo 192-8577, Japan; (S.A.); (I.S.)
| | - Chunsheng Jin
- Proteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden;
| | - Niclas G. Karlsson
- Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, 0167 Oslo, Norway;
| | - Kiyoko F. Aoki-Kinoshita
- Department of Bioinformatics, Graduate School of Engineering, Soka University, Tokyo 192-8577, Japan; (S.A.); (I.S.)
- Glycan & Life Systems Integration Center (GaLSIC), Soka University, Tokyo 192-8577, Japan
- Correspondence: (T.K.); (K.F.A.-K.)
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4
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Yadav A, Vagne Q, Sens P, Iyengar G, Rao M. Glycan processing in the Golgi: optimal information coding and constraints on cisternal number and enzyme specificity. eLife 2022; 11:76757. [PMID: 35175197 PMCID: PMC9154746 DOI: 10.7554/elife.76757] [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: 01/04/2022] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Many proteins that undergo sequential enzymatic modification in the Golgi cisternae are displayed at the plasma membrane as cell identity markers. The modified proteins, called glycans, represent a molecular code. The fidelity of this glycan code is measured by how accurately the glycan synthesis machinery realises the desired target glycan distribution for a particular cell type and niche. In this paper, we construct a simplified chemical synthesis model to quantitatively analyse the tradeoffs between the number of cisternae, and the number and specificity of enzymes, required to synthesize a prescribed target glycan distribution of a certain complexity to within a given fidelity. We find that to synthesize complex distributions, such as those observed in real cells, one needs to have multiple cisternae and precise enzyme partitioning in the Golgi. Additionally, for fixed number of enzymes and cisternae, there is an optimal level of specificity (promiscuity) of enzymes that achieves the target distribution with high fidelity. The geometry of the fidelity landscape in the multidimensional space of the number and specificity of enzymes, inter-cisternal transfer rates, and number of cisternae, provides a measure for robustness and identifies stiff and sloppy directions. Our results show how the complexity of the target glycan distribution and number of glycosylation enzymes places functional constraints on the Golgi cisternal number and enzyme specificity.
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Affiliation(s)
| | - Quentin Vagne
- Laboratoire Physico Chimie Curie, Institut Curie, CNRS UMR168, Paris, France
| | - Pierre Sens
- Laboratoire Physico Chimie Curie, Institut Curie, CNRS UMR168, Paris, France
| | - Garud Iyengar
- Industrial Engineering and Operations Research, Columbia University, New York, United States
| | - Madan Rao
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, India
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5
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West B, Wood AJ, Ungar D. Computational Modeling of Glycan Processing in the Golgi for Investigating Changes in the Arrangements of Biosynthetic Enzymes. Methods Mol Biol 2022; 2370:209-222. [PMID: 34611871 DOI: 10.1007/978-1-0716-1685-7_10] [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: 06/13/2023]
Abstract
Modeling glycan biosynthesis is becoming increasingly important due to the far-reaching implications that glycosylation can exhibit, from pathologies to biopharmaceutical manufacturing. Here we describe a stochastic simulation approach, to overcome the deterministic nature of previous models, that aims to simulate the action of glycan modifying enzymes to produce a glycan profile. This is then coupled with an approximate Bayesian computation methodology to systematically fit to empirical data in order to determine which set of parameters adequately describes the organization of enzymes within the Golgi. The model is described in detail along with a proof of concept and therapeutic applications.
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Affiliation(s)
- Ben West
- Department of Biology, University of York, York, UK
| | - A Jamie Wood
- Departments of Biology and Mathematics, University of York, York, UK
| | - Daniel Ungar
- Department of Biology, University of York, York, UK.
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6
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Aoki-Kinoshita KF. Functions of Glycosylation and Related Web Resources for Its Prediction. Methods Mol Biol 2022; 2499:135-144. [PMID: 35696078 DOI: 10.1007/978-1-0716-2317-6_6] [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: 06/15/2023]
Abstract
Glycosylation involves the attachment of carbohydrate sugar chains, or glycans, onto an amino acid residue of a protein. These glycans are often branched structures and serve to modulate the function of proteins. Glycans are synthesized through a complex process of enzymatic reactions that occur in the Golgi apparatus in mammalian systems. Because there is currently no sequencer for glycans, technologies such as mass spectrometry is used to characterize glycans in a biological sample to ascertain its glycome. This is a tedious process that requires high levels of expertise and equipment. Thus, the enzymes that work on glycans, called glycogenes or glycoenzymes, have been studied to better understand glycan function. With the development of glycan-related databases and a glycan repository, bioinformatics approaches have attempted to predict the glycosylation pathway and the glycosylation sites on proteins. This chapter introduces these methods and related Web resources for understanding glycan function.
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7
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Chiang AWT, Baghdassarian HM, Kellman BP, Bao B, Sorrentino JT, Liang C, Kuo CC, Masson HO, Lewis NE. Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy. J Biomed Sci 2021; 28:50. [PMID: 34158025 PMCID: PMC8218521 DOI: 10.1186/s12929-021-00746-2] [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: 01/15/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
Cancer immunotherapy has revolutionized treatment and led to an unprecedented wave of immuno-oncology research during the past two decades. In 2018, two pioneer immunotherapy innovators, Tasuku Honjo and James P. Allison, were awarded the Nobel Prize for their landmark cancer immunotherapy work regarding “cancer therapy by inhibition of negative immune regulation” –CTLA4 and PD-1 immune checkpoints. However, the challenge in the coming decade is to develop cancer immunotherapies that can more consistently treat various patients and cancer types. Overcoming this challenge requires a systemic understanding of the underlying interactions between immune cells, tumor cells, and immunotherapeutics. The role of aberrant glycosylation in this process, and how it influences tumor immunity and immunotherapy is beginning to emerge. Herein, we review current knowledge of miRNA-mediated regulatory mechanisms of glycosylation machinery, and how these carbohydrate moieties impact immune cell and tumor cell interactions. We discuss these insights in the context of clinical findings and provide an outlook on modulating the regulation of glycosylation to offer new therapeutic opportunities. Finally, in the coming age of systems glycobiology, we highlight how emerging technologies in systems glycobiology are enabling deeper insights into cancer immuno-oncology, helping identify novel drug targets and key biomarkers of cancer, and facilitating the rational design of glyco-immunotherapies. These hold great promise clinically in the immuno-oncology field.
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Affiliation(s)
- Austin W T Chiang
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA. .,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.
| | - Hratch M Baghdassarian
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - James T Sorrentino
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Chenguang Liang
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Chih-Chung Kuo
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Helen O Masson
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, 9500 Gilman Drive MC 0760, La Jolla, San Diego, CA, 92093, USA.,The Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, San Diego, CA, 92093, USA.,Department of Bioengineering, University of California, La Jolla, San Diego, CA, 92093, USA.,The National Biologics Facility, Technical University of Denmark, Kongens Lyngby, Denmark
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8
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Aoki-Kinoshita KF. Glycome informatics: using systems biology to gain mechanistic insights into glycan biosynthesis. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100683] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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9
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Fung Shek C, Kotidis P, Betenbaugh M. Mechanistic and data-driven modeling of protein glycosylation. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100690] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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10
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Štor J, Ruckerbauer DE, Széliová D, Zanghellini J, Borth N. Towards rational glyco-engineering in CHO: from data to predictive models. Curr Opin Biotechnol 2021; 71:9-17. [PMID: 34048995 DOI: 10.1016/j.copbio.2021.05.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/26/2021] [Accepted: 05/07/2021] [Indexed: 12/22/2022]
Abstract
Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.
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Affiliation(s)
- Jerneja Štor
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria
| | - David E Ruckerbauer
- acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria
| | - Diana Széliová
- acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria
| | - Jürgen Zanghellini
- acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria.
| | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria.
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11
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Huang YF, Aoki K, Akase S, Ishihara M, Liu YS, Yang G, Kizuka Y, Mizumoto S, Tiemeyer M, Gao XD, Aoki-Kinoshita KF, Fujita M. Global mapping of glycosylation pathways in human-derived cells. Dev Cell 2021; 56:1195-1209.e7. [PMID: 33730547 PMCID: PMC8086148 DOI: 10.1016/j.devcel.2021.02.023] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 12/15/2020] [Accepted: 02/12/2021] [Indexed: 01/02/2023]
Abstract
Glycans are one of the fundamental classes of macromolecules and are involved in a broad range of biological phenomena. A large variety of glycan structures can be synthesized depending on tissue or cell types and environmental changes. Here, we developed a comprehensive glycosylation mapping tool, termed GlycoMaple, to visualize and estimate glycan structures based on gene expression. We informatically selected 950 genes involved in glycosylation and its regulation. Expression profiles of these genes were mapped onto global glycan metabolic pathways to predict glycan structures, which were confirmed using glycomic analyses. Based on the predictions of N-glycan processing, we constructed 40 knockout HEK293 cell lines and analyzed the effects of gene knockout on glycan structures. Finally, the glycan structures of 64 cell lines, 37 tissues, and primary colon tumor tissues were estimated and compared using publicly available databases. Our systematic approach can accelerate glycan analyses and engineering in mammalian cells.
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Affiliation(s)
- Yi-Fan Huang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Sachiko Akase
- Graduate School of Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
| | - Mayumi Ishihara
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Yi-Shi Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Ganglong Yang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Yasuhiko Kizuka
- Center for Highly Advanced Integration of Nano and Life Sciences (G-CHAIN), Gifu University, Gifu 501-1193, Japan; Institute for Glyco-core Research (iGCORE), Gifu University, Gifu 501-1193, Japan
| | - Shuji Mizumoto
- Department of Pathobiochemistry, Faculty of Pharmacy, Meijo University, Nagoya, Aichi 468-8503, Japan
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Xiao-Dong Gao
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Kiyoko F Aoki-Kinoshita
- Graduate School of Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan; Glycan & Life System Integration Center (GaLSIC), Faculty of Science and Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan.
| | - Morihisa Fujita
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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12
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McDonald AG, Davey GP. Simulating the enzymes of ganglioside biosynthesis with Glycologue. Beilstein J Org Chem 2021; 17:739-748. [PMID: 33828618 PMCID: PMC8008095 DOI: 10.3762/bjoc.17.64] [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: 12/21/2020] [Accepted: 03/12/2021] [Indexed: 02/03/2023] Open
Abstract
Gangliosides are an important class of sialylated glycosphingolipids linked to ceramide that are a component of the mammalian cell surface, especially those of the central nervous system, where they function in intercellular recognition and communication. We describe an in silico method for determining the metabolic pathways leading to the most common gangliosides, based on the known enzymes of their biosynthesis. A network of 41 glycolipids is produced by the actions of the 10 enzymes included in the model. The different ganglioside nomenclature systems in common use are compared and a systematic variant of the widely used Svennerholm nomenclature is described. Knockouts of specific enzyme activities are used to simulate congenital defects in ganglioside biosynthesis, and altered ganglioside status in cancer, and the effects on network structure are predicted. The simulator is available at the Glycologue website, https://glycologue.org/.
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Affiliation(s)
- Andrew G McDonald
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland
| | - Gavin P Davey
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland
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13
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Insights into Bioinformatic Applications for Glycosylation: Instigating an Awakening towards Applying Glycoinformatic Resources for Cancer Diagnosis and Therapy. Int J Mol Sci 2020; 21:ijms21249336. [PMID: 33302373 PMCID: PMC7762546 DOI: 10.3390/ijms21249336] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/26/2020] [Accepted: 12/01/2020] [Indexed: 01/10/2023] Open
Abstract
Glycosylation plays a crucial role in various diseases and their etiology. This has led to a clear understanding on the functions of carbohydrates in cell communication, which eventually will result in novel therapeutic approaches for treatment of various disease. Glycomics has now become one among the top ten technologies that will change the future. The direct implication of glycosylation as a hallmark of cancer and for cancer therapy is well established. As in proteomics, where bioinformatics tools have led to revolutionary achievements, bioinformatics resources for glycosylation have improved its practical implication. Bioinformatics tools, algorithms and databases are a mandatory requirement to manage and successfully analyze large amount of glycobiological data generated from glycosylation studies. This review consolidates all the available tools and their applications in glycosylation research. The achievements made through the use of bioinformatics into glycosylation studies are also presented. The importance of glycosylation in cancer diagnosis and therapy is discussed and the gap in the application of widely available glyco-informatic tools for cancer research is highlighted. This review is expected to bring an awakening amongst glyco-informaticians as well as cancer biologists to bridge this gap, to exploit the available glyco-informatic tools for cancer.
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14
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Dasgupta A, Chowdhury N, De RK. Metabolic pathway engineering: Perspectives and applications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 192:105436. [PMID: 32199314 DOI: 10.1016/j.cmpb.2020.105436] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 02/29/2020] [Accepted: 03/03/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering. CONCLUSION In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.
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Affiliation(s)
- Abhijit Dasgupta
- Department of Data Science, School of Interdisciplinary Studies, University of Kalyani, Kalyani, Nadia 741235, West Bengal, India
| | - Nirmalya Chowdhury
- Department of Computer Science & Engineering, Jadavpur University, Kolkata 700032, India
| | - Rajat K De
- Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India.
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15
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Jaroentomeechai T, Taw MN, Li M, Aquino A, Agashe N, Chung S, Jewett MC, DeLisa MP. Cell-Free Synthetic Glycobiology: Designing and Engineering Glycomolecules Outside of Living Cells. Front Chem 2020; 8:645. [PMID: 32850660 PMCID: PMC7403607 DOI: 10.3389/fchem.2020.00645] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/22/2020] [Indexed: 12/12/2022] Open
Abstract
Glycans and glycosylated biomolecules are directly involved in almost every biological process as well as the etiology of most major diseases. Hence, glycoscience knowledge is essential to efforts aimed at addressing fundamental challenges in understanding and improving human health, protecting the environment and enhancing energy security, and developing renewable and sustainable resources that can serve as the source of next-generation materials. While much progress has been made, there remains an urgent need for new tools that can overexpress structurally uniform glycans and glycoconjugates in the quantities needed for characterization and that can be used to mechanistically dissect the enzymatic reactions and multi-enzyme assembly lines that promote their construction. To address this technology gap, cell-free synthetic glycobiology has emerged as a simplified and highly modular framework to investigate, prototype, and engineer pathways for glycan biosynthesis and biomolecule glycosylation outside the confines of living cells. From nucleotide sugars to complex glycoproteins, we summarize here recent efforts that harness the power of cell-free approaches to design, build, test, and utilize glyco-enzyme reaction networks that produce desired glycomolecules in a predictable and controllable manner. We also highlight novel cell-free methods for shedding light on poorly understood aspects of diverse glycosylation processes and engineering these processes toward desired outcomes. Taken together, cell-free synthetic glycobiology represents a promising set of tools and techniques for accelerating basic glycoscience research (e.g., deciphering the "glycan code") and its application (e.g., biomanufacturing high-value glycomolecules on demand).
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Affiliation(s)
- Thapakorn Jaroentomeechai
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, United States
| | - May N. Taw
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, United States
| | - Mingji Li
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, United States
| | - Alicia Aquino
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, United States
| | - Ninad Agashe
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, United States
| | - Sean Chung
- Graduate Field of Biochemistry, Molecular and Cell Biology, Cornell University, Ithaca, NY, United States
| | - Michael C. Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
- Center for Synthetic Biology, Northwestern University, Evanston, IL, United States
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, United States
| | - Matthew P. DeLisa
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, United States
- Graduate Field of Biochemistry, Molecular and Cell Biology, Cornell University, Ithaca, NY, United States
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16
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Ma B, Guan X, Li Y, Shang S, Li J, Tan Z. Protein Glycoengineering: An Approach for Improving Protein Properties. Front Chem 2020; 8:622. [PMID: 32793559 PMCID: PMC7390894 DOI: 10.3389/fchem.2020.00622] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/15/2020] [Indexed: 12/12/2022] Open
Abstract
Natural proteins are an important source of therapeutic agents and industrial enzymes. While many of them have the potential to be used as highly effective medical treatments for a wide range of diseases or as catalysts for conversion of a range of molecules into important product types required by modern society, problems associated with poor biophysical and biological properties have limited their applications. Engineering proteins with reduced side-effects and/or improved biophysical and biological properties is therefore of great importance. As a common protein modification, glycosylation has the capacity to greatly influence these properties. Over the past three decades, research from many disciplines has established the importance of glycoengineering in overcoming the limitations of proteins. In this review, we will summarize the methods that have been used to glycoengineer proteins and briefly discuss some representative examples of these methods, with the goal of providing a general overview of this research area.
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Affiliation(s)
- Bo Ma
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoyang Guan
- Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder, CO, United States
| | - Yaohao Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder, CO, United States
| | - Shiying Shang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Jing Li
- Beijing Key Laboratory of DNA Damage Response and College of Life Sciences, Capital Normal University, Beijing, China
| | - Zhongping Tan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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17
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Modeling Glycan Processing Reveals Golgi-Enzyme Homeostasis upon Trafficking Defects and Cellular Differentiation. Cell Rep 2020; 27:1231-1243.e6. [PMID: 31018136 PMCID: PMC6486481 DOI: 10.1016/j.celrep.2019.03.107] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/24/2019] [Accepted: 03/27/2019] [Indexed: 01/11/2023] Open
Abstract
The decoration of proteins by carbohydrates is essential for eukaryotic life yet heterogeneous due to a lack of biosynthetic templates. This complex carbohydrate mixture—the glycan profile—is generated in the compartmentalized Golgi, in which level and localization of glycosylation enzymes are key determinants. Here, we develop and validate a computational model for glycan biosynthesis to probe how the biosynthetic machinery creates different glycan profiles. We combined stochastic modeling with Bayesian fitting that enables rigorous comparison to experimental data despite starting with uncertain initial parameters. This is an important development in the field of glycan modeling, which revealed biological insights about the glycosylation machinery in altered cellular states. We experimentally validated changes in N-linked glycan-modifying enzymes in cells with perturbed intra-Golgi-enzyme sorting and the predicted glycan-branching activity during osteogenesis. Our model can provide detailed information on altered biosynthetic paths, with potential for advancing treatments for glycosylation-related diseases and glyco-engineering of cells. Developed a stochastic model of N-glycosylation coupled with Bayesian fitting Validated predicted changes of Golgi organization in trafficking mutants Model pinpointed functionally relevant glycan alterations in osteogenesis
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18
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Majewska NI, Tejada ML, Betenbaugh MJ, Agarwal N. N-Glycosylation of IgG and IgG-Like Recombinant Therapeutic Proteins: Why Is It Important and How Can We Control It? Annu Rev Chem Biomol Eng 2020; 11:311-338. [DOI: 10.1146/annurev-chembioeng-102419-010001] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Regulatory bodies worldwide consider N-glycosylation to be a critical quality attribute for immunoglobulin G (IgG) and IgG-like therapeutics. This consideration is due to the importance of posttranslational modifications in determining the efficacy, safety, and pharmacokinetic properties of biologics. Given its critical role in protein therapeutic production, we review N-glycosylation beginning with an overview of the myriad interactions of N-glycans with other biological factors. We examine the mechanism and drivers for N-glycosylation during biotherapeutic production and the several competing factors that impact glycan formation, including the abundance of precursor nucleotide sugars, transporters, glycosidases, glycosyltransferases, and process conditions. We explore the role of these factors with a focus on the analytical approaches used to characterize glycosylation and associated processes, followed by the current state of advanced glycosylation modeling techniques. This combination of disciplines allows for a deeper understanding of N-glycosylation and will lead to more rational glycan control.
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Affiliation(s)
- Natalia I. Majewska
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;,
- Cell Culture and Fermentation Sciences, AstraZeneca, Gaithersburg, Maryland 20878, USA
| | - Max L. Tejada
- Bioassay, Impurities and Quality, AstraZeneca, Gaithersburg, Maryland 20878, USA
| | - Michael J. Betenbaugh
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;,
| | - Nitin Agarwal
- Cell Culture and Fermentation Sciences, AstraZeneca, Gaithersburg, Maryland 20878, USA
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19
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Kotidis P, Kontoravdi C. Harnessing the potential of artificial neural networks for predicting protein glycosylation. Metab Eng Commun 2020; 10:e00131. [PMID: 32489858 PMCID: PMC7256630 DOI: 10.1016/j.mec.2020.e00131] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/06/2020] [Accepted: 05/06/2020] [Indexed: 12/16/2022] Open
Abstract
Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.
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20
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Gupta R, Leon F, Rauth S, Batra SK, Ponnusamy MP. A Systematic Review on the Implications of O-linked Glycan Branching and Truncating Enzymes on Cancer Progression and Metastasis. Cells 2020; 9:E446. [PMID: 32075174 PMCID: PMC7072808 DOI: 10.3390/cells9020446] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/10/2020] [Accepted: 02/12/2020] [Indexed: 12/27/2022] Open
Abstract
Glycosylation is the most commonly occurring post-translational modifications, and is believed to modify over 50% of all proteins. The process of glycan modification is directed by different glycosyltransferases, depending on the cell in which it is expressed. These small carbohydrate molecules consist of multiple glycan families that facilitate cell-cell interactions, protein interactions, and downstream signaling. An alteration of several types of O-glycan core structures have been implicated in multiple cancers, largely due to differential glycosyltransferase expression or activity. Consequently, aberrant O-linked glycosylation has been extensively demonstrated to affect biological function and protein integrity that directly result in cancer growth and progression of several diseases. Herein, we provide a comprehensive review of several initiating enzymes involved in the synthesis of O-linked glycosylation that significantly contribute to a number of different cancers.
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Affiliation(s)
- Rohitesh Gupta
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (R.G.); (F.L.); (S.R.)
| | - Frank Leon
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (R.G.); (F.L.); (S.R.)
| | - Sanchita Rauth
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (R.G.); (F.L.); (S.R.)
| | - Surinder K. Batra
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (R.G.); (F.L.); (S.R.)
- Fred and Pamela Buffett Cancer Center, Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 681980-5900, USA
- Department of Pathology and Microbiology, UNMC, Omaha, NE 68198-5900, USA
| | - Moorthy P. Ponnusamy
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (R.G.); (F.L.); (S.R.)
- Fred and Pamela Buffett Cancer Center, Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 681980-5900, USA
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21
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A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering. CURRENT RESEARCH IN BIOTECHNOLOGY 2020; 2:22-36. [PMID: 32285041 DOI: 10.1016/j.crbiot.2020.01.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modelling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.
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22
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Kontoravdi C, Jimenez del Val I. Computational tools for predicting and controlling the glycosylation of biopharmaceuticals. Curr Opin Chem Eng 2018. [DOI: 10.1016/j.coche.2018.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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23
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Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture. Processes (Basel) 2018. [DOI: 10.3390/pr6100176] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
The terminal sugar molecules of the N-linked glycan attached to the fragment crystalizable (Fc) region is a critical quality attribute of therapeutic monoclonal antibodies (mAbs) such as immunoglobulin G (IgG). There exists naturally-occurring heterogeneity in the N-linked glycan structure of mAbs, and such heterogeneity has a significant influence on the clinical safety and efficacy of mAb drugs. We previously proposed a constraint-based modeling method called glycosylation flux analysis (GFA) to characterize the rates (fluxes) of intracellular glycosylation reactions. One contribution of this work is a significant improvement in the computational efficiency of the GFA, which is beneficial for analyzing large datasets. Another contribution of our study is the analysis of IgG glycosylation in continuous perfusion Chinese Hamster Ovary (CHO) cell cultures. The GFA of the perfusion cell culture data indicated that the dynamical changes of IgG glycan heterogeneity are mostly attributed to alterations in the galactosylation flux activity. By using a random forest regression analysis of the IgG galactosylation flux activity, we were further able to link the dynamics of galactosylation with two process parameters: cell-specific productivity of IgG and extracellular ammonia concentration. The characteristics of IgG galactosylation dynamics agree well with what we previously reported for fed-batch cultivations of the same CHO cell strain.
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24
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McDonald AG, Tipton KF, Davey GP. A mechanism for bistability in glycosylation. PLoS Comput Biol 2018; 14:e1006348. [PMID: 30074989 PMCID: PMC6093706 DOI: 10.1371/journal.pcbi.1006348] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 08/15/2018] [Accepted: 07/04/2018] [Indexed: 12/29/2022] Open
Abstract
Glycosyltransferases are a class of enzymes that catalyse the posttranslational modification of proteins to produce a large number of glycoconjugate acceptors from a limited number of nucleotide-sugar donors. The products of one glycosyltransferase can be the substrates of several other enzymes, causing a combinatorial explosion in the number of possible glycan products. The kinetic behaviour of systems where multiple acceptor substrates compete for a single enzyme is presented, and the case in which high concentrations of an acceptor substrate are inhibitory as a result of abortive complex formation, is shown to result in non-Michaelian kinetics that can lead to bistability in an open system. A kinetic mechanism is proposed that is consistent with the available experimental evidence and provides a possible explanation for conflicting observations on the β-1,4-galactosyltransferases. Abrupt switching between steady states in networks of glycosyltransferase-catalysed reactions may account for the observed changes in glycosyl-epitopes in cancer cells.
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Affiliation(s)
- Andrew G. McDonald
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
- * E-mail: (AGM); (GPD)
| | - Keith F. Tipton
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
| | - Gavin P. Davey
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
- * E-mail: (AGM); (GPD)
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25
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Kremkow BG, Lee KH. Glyco-Mapper: A Chinese hamster ovary (CHO) genome-specific glycosylation prediction tool. Metab Eng 2018. [PMID: 29522825 DOI: 10.1016/j.ymben.2018.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Glyco-Mapper is a novel systems biology product quality prediction tool created using a new framework termed: Discretized Reaction Network Modeling using Fuzzy Parameters (DReaM-zyP). Within Glyco-Mapper, users fix the nutrient feed composition and the glycosylation reaction fluxes to fit the model glycoform to the reference experimental glycoform, enabling cell-line specific glycoform predictions as a result of cell engineering strategies. Glyco-Mapper accurately predicts glycoforms associated with genetic alterations that result in the appearance or disappearance of one or more glycans with an accuracy, sensitivity, and specificity of 96%, 85%, and 97%, respectively, for publications between 1999 and 2014. The modeled glycoforms span a large range of glycoform engineering strategies, including the altered expression of glycosylation, nucleotide sugar transport, and metabolism genes, as well as an altered nutrient feeding strategy. A glycoprotein-producing CHO cell line reference glycoform was modeled and a novel Glyco-Mapper prediction was experimentally confirmed with an accuracy and specificity of 95% and 98%, respectively. Glyco-Mapper is a product quality prediction tool that provides a streamlined way to design host cell line genomes to achieve specific product quality attributes.
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Affiliation(s)
- Benjamin G Kremkow
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA; Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA
| | - Kelvin H Lee
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA; Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA.
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26
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Tejwani V, Andersen MR, Nam JH, Sharfstein ST. Glycoengineering in CHO Cells: Advances in Systems Biology. Biotechnol J 2018; 13:e1700234. [PMID: 29316325 DOI: 10.1002/biot.201700234] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 12/28/2017] [Indexed: 12/19/2022]
Abstract
For several decades, glycoprotein biologics have been successfully produced from Chinese hamster ovary (CHO) cells. The therapeutic efficacy and potency of glycoprotein biologics are often dictated by their post-translational modifications, particularly glycosylation, which unlike protein synthesis, is a non-templated process. Consequently, both native and recombinant glycoprotein production generate heterogeneous mixtures containing variable amounts of different glycoforms. Stability, potency, plasma half-life, and immunogenicity of the glycoprotein biologic are directly influenced by the glycoforms. Recently, CHO cells have also been explored for production of therapeutic glycosaminoglycans (e.g., heparin), which presents similar challenges as producing glycoproteins biologics. Approaches to controlling heterogeneity in CHO cells and directing the biosynthetic process toward desired glycoforms are not well understood. A systems biology approach combining different technologies is needed for complete understanding of the molecular processes accounting for this variability and to open up new venues in cell line development. In this review, we describe several advances in genetic manipulation, modeling, and glycan and glycoprotein analysis that together will provide new strategies for glycoengineering of CHO cells with desired or enhanced glycosylation capabilities.
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Affiliation(s)
- Vijay Tejwani
- Colleges of Nanoscale Science and Engineering, SUNY Polytechnic Institute, 257 Fuller Road, Albany, NY, 12203, USA
| | - Mikael R Andersen
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | | | - Susan T Sharfstein
- Colleges of Nanoscale Science and Engineering, SUNY Polytechnic Institute, 257 Fuller Road, Albany, NY, 12203, USA
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27
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Glycosylation flux analysis reveals dynamic changes of intracellular glycosylation flux distribution in Chinese hamster ovary fed-batch cultures. Metab Eng 2017; 43:9-20. [PMID: 28754360 DOI: 10.1016/j.ymben.2017.07.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 06/29/2017] [Accepted: 07/20/2017] [Indexed: 01/06/2023]
Abstract
N-linked glycosylation of proteins has both functional and structural significance. Importantly, the glycan structure of a therapeutic protein influences its efficacy, pharmacokinetics, pharmacodynamics and immunogenicity. In this work, we developed glycosylation flux analysis (GFA) for predicting intracellular production and consumption rates (fluxes) of glycoforms, and applied this analysis to CHO fed-batch immunoglobulin G (IgG) production using two different media compositions, with and without additional manganese feeding. The GFA is based on a constraint-based modeling of the glycosylation network, employing a pseudo steady state assumption. While the glycosylation fluxes in the network are balanced at each time point, the GFA allows the fluxes to vary with time by way of two scaling factors: (1) an enzyme-specific factor that captures the temporal changes among glycosylation reactions catalysed by the same enzyme, and (2) the cell specific productivity factor that accounts for the dynamic changes in the IgG production rate. The GFA of the CHO fed-batch cultivations showed that regardless of the media composition, galactosylation fluxes decreased with the cultivation time more significantly than the other glycosylation reactions. Furthermore, the GFA showed that the addition of Mn, a cofactor of galactosyltransferase, has the effect of increasing the galactosylation fluxes but only during the beginning of the cultivation period. The results thus demonstrated the power of the GFA in delineating the dynamic alterations of the glycosylation fluxes by local (enzyme-specific) and global (cell specific productivity) factors.
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28
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Karst DJ, Scibona E, Serra E, Bielser JM, Souquet J, Stettler M, Broly H, Soos M, Morbidelli M, Villiger TK. Modulation and modeling of monoclonal antibody N-linked glycosylation in mammalian cell perfusion reactors. Biotechnol Bioeng 2017; 114:1978-1990. [DOI: 10.1002/bit.26315] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 02/24/2017] [Accepted: 04/09/2017] [Indexed: 12/23/2022]
Affiliation(s)
- Daniel J. Karst
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering; ETH Zurich; HCI F-129, Vladimir-Prelog-Weg 1 8093 Zurich Switzerland
| | - Ernesto Scibona
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering; ETH Zurich; HCI F-129, Vladimir-Prelog-Weg 1 8093 Zurich Switzerland
| | - Elisa Serra
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering; ETH Zurich; HCI F-129, Vladimir-Prelog-Weg 1 8093 Zurich Switzerland
| | - Jean-Marc Bielser
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering; ETH Zurich; HCI F-129, Vladimir-Prelog-Weg 1 8093 Zurich Switzerland
- Merck Serono SA; Biotech Process Sciences, ZI B 1809; Corsier-sur-Vevey Switzerland
| | - Jonathan Souquet
- Merck Serono SA; Biotech Process Sciences, ZI B 1809; Corsier-sur-Vevey Switzerland
| | - Matthieu Stettler
- Merck Serono SA; Biotech Process Sciences, ZI B 1809; Corsier-sur-Vevey Switzerland
| | - Hervé Broly
- Merck Serono SA; Biotech Process Sciences, ZI B 1809; Corsier-sur-Vevey Switzerland
| | - Miroslav Soos
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering; ETH Zurich; HCI F-129, Vladimir-Prelog-Weg 1 8093 Zurich Switzerland
- Department of Chemical Engineering; University of Chemistry and Technology; Technicka 3, 166 28 Prague Czech Republic
| | - Massimo Morbidelli
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering; ETH Zurich; HCI F-129, Vladimir-Prelog-Weg 1 8093 Zurich Switzerland
| | - Thomas K. Villiger
- Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering; ETH Zurich; HCI F-129, Vladimir-Prelog-Weg 1 8093 Zurich Switzerland
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29
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Krambeck FJ, Bennun SV, Andersen MR, Betenbaugh MJ. Model-based analysis of N-glycosylation in Chinese hamster ovary cells. PLoS One 2017; 12:e0175376. [PMID: 28486471 PMCID: PMC5423595 DOI: 10.1371/journal.pone.0175376] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 03/26/2017] [Indexed: 11/19/2022] Open
Abstract
The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products.
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Affiliation(s)
- Frederick J. Krambeck
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- ReacTech Inc., Alexandria, Virginia, United States of America
| | - Sandra V. Bennun
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- ReacTech Inc., Alexandria, Virginia, United States of America
| | - Mikael R. Andersen
- Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
| | - Michael J. Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
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Sommeregger W, Sissolak B, Kandra K, von Stosch M, Mayer M, Striedner G. Quality by control: Towards model predictive control of mammalian cell culture bioprocesses. Biotechnol J 2017; 12. [DOI: 10.1002/biot.201600546] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/17/2017] [Accepted: 03/09/2017] [Indexed: 11/05/2022]
Affiliation(s)
| | - Bernhard Sissolak
- DBT - University of Natural Resources and Life Sciences (BOKU); Vienna Austria
| | - Kulwant Kandra
- DBT - University of Natural Resources and Life Sciences (BOKU); Vienna Austria
| | | | | | - Gerald Striedner
- DBT - University of Natural Resources and Life Sciences (BOKU); Vienna Austria
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31
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Galleguillos SN, Ruckerbauer D, Gerstl MP, Borth N, Hanscho M, Zanghellini J. What can mathematical modelling say about CHO metabolism and protein glycosylation? Comput Struct Biotechnol J 2017; 15:212-221. [PMID: 28228925 PMCID: PMC5310201 DOI: 10.1016/j.csbj.2017.01.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Revised: 01/09/2017] [Accepted: 01/12/2017] [Indexed: 11/15/2022] Open
Abstract
Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation.
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Affiliation(s)
- Sarah N Galleguillos
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - David Ruckerbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Matthias P Gerstl
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Nicole Borth
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Michael Hanscho
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Jürgen Zanghellini
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria
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32
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How to Use Mechanistic Metabolic Modeling to Ensure High Quality Glycoprotein Production. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/b978-0-444-63965-3.50475-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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33
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Hayes JM, Wormald MR, Rudd PM, Davey GP. Fc gamma receptors: glycobiology and therapeutic prospects. J Inflamm Res 2016; 9:209-219. [PMID: 27895507 PMCID: PMC5118039 DOI: 10.2147/jir.s121233] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Therapeutic antibodies hold great promise for the treatment of cancer and autoimmune diseases, and developments in antibody–drug conjugates and bispecific antibodies continue to enhance treatment options for patients. Immunoglobulin (Ig) G antibodies are proteins with complex modifications, which have a significant impact on their function. The most important of these modifications is glycosylation, the addition of conserved glycans to the antibody Fc region, which is critical for its interaction with the immune system and induction of effector activities such as antibody-dependent cell cytotoxicity, complement activation and phagocytosis. Communication of IgG antibodies with the immune system is controlled and mediated by Fc gamma receptors (FcγRs), membrane-bound proteins, which relay the information sensed and gathered by antibodies to the immune system. These receptors are also glycoproteins and provide a link between the innate and adaptive immune systems. Recent information suggests that this receptor glycan modification is also important for the interaction with antibodies and downstream immune response. In this study, the current knowledge on FcγR glycosylation is discussed, and some insight into its role and influence on the interaction properties with IgG, particularly in the context of biotherapeutics, is provided. For the purpose of this study, other Fc receptors such as FcαR, FcεR or FcRn are not discussed extensively, as IgG-based antibodies are currently the only therapeutic antibody-based products on the market. In addition, FcγRs as therapeutics and therapeutic targets are discussed, and insight into and comment on the therapeutic aspects of receptor glycosylation are provided.
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Affiliation(s)
- Jerrard M Hayes
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College, Dublin, Ireland
| | - Mark R Wormald
- Department of Biochemistry, Oxford Glycobiology Institute, University of Oxford, Oxford, UK
| | - Pauline M Rudd
- NIBRT Glycoscience Group, National Institute for Bioprocessing, Research and Training, Dublin, Ireland
| | - Gavin P Davey
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College, Dublin, Ireland
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34
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Batra J, Rathore AS. Glycosylation of monoclonal antibody products: Current status and future prospects. Biotechnol Prog 2016; 32:1091-1102. [DOI: 10.1002/btpr.2366] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 09/04/2016] [Indexed: 12/31/2022]
Affiliation(s)
- Jyoti Batra
- Department of Chemical Engineering; Indian Institute of Technology; Hauz Khas New Delhi India
| | - Anurag S. Rathore
- Department of Chemical Engineering; Indian Institute of Technology; Hauz Khas New Delhi India
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35
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McDonald AG, Hayes JM, Davey GP. Metabolic flux control in glycosylation. Curr Opin Struct Biol 2016; 40:97-103. [DOI: 10.1016/j.sbi.2016.08.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 08/04/2016] [Accepted: 08/29/2016] [Indexed: 11/17/2022]
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36
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Hou W, Qiu Y, Hashimoto N, Ching WK, Aoki-Kinoshita KF. A systematic framework to derive N-glycan biosynthesis process and the automated construction of glycosylation networks. BMC Bioinformatics 2016; 17 Suppl 7:240. [PMID: 27454116 PMCID: PMC4965717 DOI: 10.1186/s12859-016-1094-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Abnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental data for the identification of glycoforms contributing to disease. To overcome this limitation, the automatic construction of glycosylation reaction networks in silico is a critical step. Results In this paper, a framework K2014 is developed to automatically construct N-glycosylation networks in MATLAB with the involvement of the 27 most-known enzyme reaction rules of 22 enzymes, as an extension of previous model KB2005. A toolbox named Glycosylation Network Analysis Toolbox (GNAT) is applied to define network properties systematically, including linkages, stereochemical specificity and reaction conditions of enzymes. Our network shows a strong ability to predict a wider range of glycans produced by the enzymes encountered in the Golgi Apparatus in human cell expression systems. Conclusions Our results demonstrate a better understanding of the underlying glycosylation process and the potential of systems glycobiology tools for analyzing conventional biochemical or mass spectrometry-based experimental data quantitatively in a more realistic and practical way.
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Affiliation(s)
- Wenpin Hou
- Department of Mathematics, The University of Hong Kong, Hong Kong, 999077, China.
| | - Yushan Qiu
- Hematology Oncology Division, Northwestern University, Evanston, IL 60208, USA
| | - Nobuyuki Hashimoto
- Faculty of Science and Engineering, Soka University, Tokyo, 192-8577, Japan
| | - Wai-Ki Ching
- Department of Mathematics, The University of Hong Kong, Hong Kong, 999077, China
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37
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Villiger TK, Scibona E, Stettler M, Broly H, Morbidelli M, Soos M. Controlling the time evolution of mAb N-linked glycosylation - Part II: Model-based predictions. Biotechnol Prog 2016; 32:1135-1148. [PMID: 27273889 DOI: 10.1002/btpr.2315] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 03/24/2016] [Indexed: 01/04/2023]
Abstract
N-linked glycosylation is known to be a crucial factor for the therapeutic efficacy and safety of monoclonal antibodies (mAbs) and many other glycoproteins. The nontemplate process of glycosylation is influenced by external factors which have to be tightly controlled during the manufacturing process. In order to describe and predict mAb N-linked glycosylation patterns in a CHO-S cell fed-batch process, an existing dynamic mathematical model has been refined and coupled to an unstructured metabolic model. High-throughput cell culture experiments carried out in miniaturized bioreactors in combination with intracellular measurements of nucleotide sugars were used to tune the parameter configuration of the coupled models as a function of extracellular pH, manganese and galactose addition. The proposed modeling framework is able to predict the time evolution of N-linked glycosylation patterns during a fed-batch process as a function of time as well as the manipulated variables. A constant and varying mAb N-linked glycosylation pattern throughout the culture were chosen to demonstrate the predictive capability of the modeling framework, which is able to quantify the interconnected influence of media components and cell culture conditions. Such a model-based evaluation of feeding regimes using high-throughput tools and mathematical models gives rise to a more rational way to control and design cell culture processes with defined glycosylation patterns. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1135-1148, 2016.
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Affiliation(s)
- Thomas K Villiger
- Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Ernesto Scibona
- Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Matthieu Stettler
- Biotech Process Sciences, Merck-Serono S.A., Corsier-sur-Vevey, 1809, Switzerland
| | - Hervé Broly
- Biotech Process Sciences, Merck-Serono S.A., Corsier-sur-Vevey, 1809, Switzerland
| | - Massimo Morbidelli
- Dept. of Chemistry and Applied Biosciences, Inst. for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Miroslav Soos
- Dept. of Chemical Engineering, University of Chemistry and Technology, Prague, Czech Republic.
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38
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Akune Y, Lin CH, Abrahams JL, Zhang J, Packer NH, Aoki-Kinoshita KF, Campbell MP. Comprehensive analysis of the N-glycan biosynthetic pathway using bioinformatics to generate UniCorn: A theoretical N-glycan structure database. Carbohydr Res 2016; 431:56-63. [PMID: 27318307 DOI: 10.1016/j.carres.2016.05.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 05/23/2016] [Accepted: 05/29/2016] [Indexed: 02/06/2023]
Abstract
Glycan structures attached to proteins are comprised of diverse monosaccharide sequences and linkages that are produced from precursor nucleotide-sugars by a series of glycosyltransferases. Databases of these structures are an essential resource for the interpretation of analytical data and the development of bioinformatics tools. However, with no template to predict what structures are possible the human glycan structure databases are incomplete and rely heavily on the curation of published, experimentally determined, glycan structure data. In this work, a library of 45 human glycosyltransferases was used to generate a theoretical database of N-glycan structures comprised of 15 or less monosaccharide residues. Enzyme specificities were sourced from major online databases including Kyoto Encyclopedia of Genes and Genomes (KEGG) Glycan, Consortium for Functional Glycomics (CFG), Carbohydrate-Active enZymes (CAZy), GlycoGene DataBase (GGDB) and BRENDA. Based on the known activities, more than 1.1 million theoretical structures and 4.7 million synthetic reactions were generated and stored in our database called UniCorn. Furthermore, we analyzed the differences between the predicted glycan structures in UniCorn and those contained in UniCarbKB (www.unicarbkb.org), a database which stores experimentally described glycan structures reported in the literature, and demonstrate that UniCorn can be used to aid in the assignment of ambiguous structures whilst also serving as a discovery database.
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Affiliation(s)
- Yukie Akune
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia; Department of Bioinformatics, Graduate School of Engineering, Soka University, 1-236, Tangi, Hachioji, 192-8577, Tokyo, Japan
| | - Chi-Hung Lin
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Jodie L Abrahams
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Jingyu Zhang
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Nicolle H Packer
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia
| | - Kiyoko F Aoki-Kinoshita
- Department of Bioinformatics, Graduate School of Engineering, Soka University, 1-236, Tangi, Hachioji, 192-8577, Tokyo, Japan
| | - Matthew P Campbell
- Department of Chemistry and Biomolecular Sciences, Faculty of Science & Engineering, Macquarie University, Balaclava Road, North Ryde, NSW, 2109, Australia.
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39
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Zhang P, Woen S, Wang T, Liau B, Zhao S, Chen C, Yang Y, Song Z, Wormald MR, Yu C, Rudd PM. Challenges of glycosylation analysis and control: an integrated approach to producing optimal and consistent therapeutic drugs. Drug Discov Today 2016; 21:740-65. [DOI: 10.1016/j.drudis.2016.01.006] [Citation(s) in RCA: 136] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 12/22/2015] [Accepted: 01/14/2016] [Indexed: 12/18/2022]
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40
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Sha S, Agarabi C, Brorson K, Lee DY, Yoon S. N-Glycosylation Design and Control of Therapeutic Monoclonal Antibodies. Trends Biotechnol 2016; 34:835-846. [PMID: 27016033 DOI: 10.1016/j.tibtech.2016.02.013] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 02/20/2016] [Accepted: 02/24/2016] [Indexed: 12/31/2022]
Abstract
The N-linked glycan profiles on recombinant monoclonal antibody therapeutics significantly affect antibody biological functions and are largely determined by host cell genotypes and culture conditions. A key step in bioprocess development for monoclonal antibodies (mAbs) involves optimization and control of N-glycan profiles. With pressure from pricing and biosimilars looming, more efficient and effective approaches are sought in the field of glycoengineering. Metabolic studies and mathematical modeling are two such approaches that optimize bioprocesses by better understanding and predicting glycosylation. In this review, we summarize a group of strategies currently used for glycan profile modulation and control. Metabolic analysis and mathematical modeling are then explored with an emphasis on how these two techniques can be utilized to advance glycoengineering.
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Affiliation(s)
- Sha Sha
- Biomedical Engineering and Biotechnology, University of Massachusetts Lowell, Lowell, MA 01850, USA
| | - Cyrus Agarabi
- Division of Biotechnology Review and Research II, Office of Biotechnology Products, OPQ, CDER, FDA, Silver Spring, MD, USA
| | - Kurt Brorson
- Division of Biotechnology Review and Research II, Office of Biotechnology Products, OPQ, CDER, FDA, Silver Spring, MD, USA
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive, Singapore 117585, Singapore
| | - Seongkyu Yoon
- Biomedical Engineering and Biotechnology, University of Massachusetts Lowell, Lowell, MA 01850, USA.
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41
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Zalai D, Hevér H, Lovász K, Molnár D, Wechselberger P, Hofer A, Párta L, Putics Á, Herwig C. A control strategy to investigate the relationship between specific productivity and high-mannose glycoforms in CHO cells. Appl Microbiol Biotechnol 2016; 100:7011-24. [PMID: 26910040 PMCID: PMC4947490 DOI: 10.1007/s00253-016-7380-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Revised: 02/01/2016] [Accepted: 02/03/2016] [Indexed: 12/26/2022]
Abstract
The integration of physiological knowledge into process control strategies is a cornerstone for the improvement of biopharmaceutical cell culture technologies. The present contribution investigates the applicability of specific productivity as a physiological control parameter in a cell culture process producing a monoclonal antibody (mAb) in CHO cells. In order to characterize cell physiology, the on-line oxygen uptake rate (OUR) was monitored and the time-resolved specific productivity was calculated as physiological parameters. This characterization enabled to identify the tight link between the deprivation of tyrosine and the decrease in cell respiration and in specific productivity. Subsequently, this link was used to control specific productivity by applying different feeding profiles. The maintenance of specific productivity at various levels enabled to identify a correlation between the rate of product formation and the relative abundance of high-mannose glycoforms. An increase in high mannose content was assumed to be the result of high specific productivity. Furthermore, the high mannose content as a function of cultivation pH and specific productivity was investigated in a design of experiment approach. This study demonstrated how physiological parameters could be used to understand interactions between process parameters, physiological parameters, and product quality attributes.
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Affiliation(s)
- Dénes Zalai
- Department of Biotechnology, Gedeon Richter Plc., 19-21, Gyömrői út, Budapest, 1103, Hungary.,Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1a, 1060, Vienna, Austria
| | - Helga Hevér
- Spectroscopic Research Department, Gedeon Richter Plc., 19-21, Gyömrői út, Budapest, 1103, Hungary
| | - Krisztina Lovász
- Department of Biotechnology, Gedeon Richter Plc., 19-21, Gyömrői út, Budapest, 1103, Hungary
| | - Dóra Molnár
- Department of Biotechnology, Gedeon Richter Plc., 19-21, Gyömrői út, Budapest, 1103, Hungary
| | - Patrick Wechselberger
- Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1a, 1060, Vienna, Austria.,CD Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna, Austria
| | - Alexandra Hofer
- Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1a, 1060, Vienna, Austria
| | - László Párta
- Department of Biotechnology, Gedeon Richter Plc., 19-21, Gyömrői út, Budapest, 1103, Hungary
| | - Ákos Putics
- Department of Biotechnology, Gedeon Richter Plc., 19-21, Gyömrői út, Budapest, 1103, Hungary
| | - Christoph Herwig
- Institute of Chemical Engineering, Research Area Biochemical Engineering, Vienna University of Technology, Gumpendorfer Strasse 1a, 1060, Vienna, Austria. .,CD Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, Vienna, Austria.
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42
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Jimenez Del Val I, Fan Y, Weilguny D. Dynamics of immature mAb glycoform secretion during CHO cell culture: An integrated modelling framework. Biotechnol J 2016; 11:610-23. [PMID: 26743760 DOI: 10.1002/biot.201400663] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 10/09/2015] [Accepted: 12/23/2015] [Indexed: 01/08/2023]
Abstract
Ensuring consistent glycosylation-associated quality of therapeutic monoclonal antibodies (mAbs) has become a priority in pharmaceutical bioprocessing given that the distribution and composition of the carbohydrates (glycans) bound to these molecules determines their therapeutic efficacy and immunogenicity. However, the interaction between bioprocess conditions, cellular metabolism and the intracellular process of glycosylation remains to be fully understood. To gain further insight into these interactions, we present a novel integrated modelling platform that links dynamic variations in mAb glycosylation with cellular secretory capacity. Two alternative mechanistic representations of how mAb specific productivity (qp ) influences glycosylation are compared. In the first, mAb glycosylation is modulated by the linear velocity with which secretory cargo traverses the Golgi apparatus. In the second, glycosylation is influenced by variations in Golgi volume. Within our modelling framework, both mechanisms accurately reproduce experimentally-observed dynamic changes in mAb glycosylation. In addition, an optimisation-based strategy has been developed to estimate the concentration of glycosylation enzymes required to minimise mAb glycoform variability. Our results suggest that the availability of glycosylation machinery relative to cellular secretory capacity may play a crucial role in mAb glycosylation. In the future, the modelling framework presented here may aid in selecting and engineering cell lines that ensure consistent mAb glycosylatio.
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Affiliation(s)
- Ioscani Jimenez Del Val
- School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Yuzhou Fan
- Network Engineering of Eukaryotic Cell Factories, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.,Symphogen A/S, Ballerup, Denmark
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43
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Ben Yahia B, Malphettes L, Heinzle E. Macroscopic modeling of mammalian cell growth and metabolism. Appl Microbiol Biotechnol 2015; 99:7009-24. [PMID: 26198881 PMCID: PMC4536272 DOI: 10.1007/s00253-015-6743-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Revised: 05/28/2015] [Accepted: 05/30/2015] [Indexed: 12/24/2022]
Abstract
We review major modeling strategies and methods to understand and simulate the macroscopic behavior of mammalian cells. These strategies comprise two important steps: the first step is to identify stoichiometric relationships for the cultured cells connecting the extracellular inputs and outputs. In a second step, macroscopic kinetic models are introduced. These relationships together with bioreactor and metabolite balances provide a complete description of a system in the form of a set of differential equations. These can be used for the simulation of cell culture performance and further for optimization of production.
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Affiliation(s)
- Bassem Ben Yahia
- />Biochemical Engineering Institute, Saarland University, Campus A1.5, D-66123 Saarbruecken, Germany
- />Upstream Process Sciences Biotech Sciences, UCB Pharma S.A., Avenue de l’Industrie, B-1420, Braine l’Alleud, Belgium
| | - Laetitia Malphettes
- />Upstream Process Sciences Biotech Sciences, UCB Pharma S.A., Avenue de l’Industrie, B-1420, Braine l’Alleud, Belgium
| | - Elmar Heinzle
- />Biochemical Engineering Institute, Saarland University, Campus A1.5, D-66123 Saarbruecken, Germany
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44
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Liu G, Neelamegham S. Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:163-81. [PMID: 25871730 DOI: 10.1002/wsbm.1296] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 02/08/2015] [Accepted: 03/04/2015] [Indexed: 12/22/2022]
Abstract
The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. 'Systems Glycobiology' is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or 'systems-level' view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell-surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post-translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation-related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize 'Big Data' stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole-cell level has been standardized, there is a need to integrate mass spectrometry derived site-specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between 'data' generated in wet-laboratory studies with 'knowledge' that can enhance our understanding of the glycome.
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Affiliation(s)
- Gang Liu
- Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY, USA
| | - Sriram Neelamegham
- Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY, USA
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45
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Toward genome-scale models of the Chinese hamster ovary cells: incentives, status and perspectives. ACTA ACUST UNITED AC 2014. [DOI: 10.4155/pbp.14.54] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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46
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McDonald AG, Hayes JM, Bezak T, Głuchowska SA, Cosgrave EFJ, Struwe WB, Stroop CJM, Kok H, van de Laar T, Rudd PM, Tipton KF, Davey GP. Galactosyltransferase 4 is a major control point for glycan branching in N-linked glycosylation. J Cell Sci 2014; 127:5014-26. [PMID: 25271059 PMCID: PMC4248093 DOI: 10.1242/jcs.151878] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Protein N-glycosylation is a common post-translational modification that produces a complex array of branched glycan structures. The levels of branching, or antennarity, give rise to differential biological activities for single glycoproteins. However, the precise mechanism controlling the glycan branching and glycosylation network is unknown. Here, we constructed quantitative mathematical models of N-linked glycosylation that predicted new control points for glycan branching. Galactosyltransferase, which acts on N-acetylglucosamine residues, was unexpectedly found to control metabolic flux through the glycosylation pathway and the level of final antennarity of nascent protein produced in the Golgi network. To further investigate the biological consequences of glycan branching in nascent proteins, we glycoengineered a series of mammalian cells overexpressing human chorionic gonadotropin (hCG). We identified a mechanism in which galactosyltransferase 4 isoform regulated N-glycan branching on the nascent protein, subsequently controlling biological activity in an in vivo model of hCG activity. We found that galactosyltransferase 4 is a major control point for glycan branching decisions taken in the Golgi of the cell, which might ultimately control the biological activity of nascent glycoprotein.
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Affiliation(s)
- Andrew G McDonald
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland National Institute for Bioprocessing Research and Training (NIBRT), Fosters Avenue, Dublin 4, Ireland
| | - Jerrard M Hayes
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland National Institute for Bioprocessing Research and Training (NIBRT), Fosters Avenue, Dublin 4, Ireland
| | - Tania Bezak
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland National Institute for Bioprocessing Research and Training (NIBRT), Fosters Avenue, Dublin 4, Ireland
| | - Sonia A Głuchowska
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
| | - Eoin F J Cosgrave
- National Institute for Bioprocessing Research and Training (NIBRT), Fosters Avenue, Dublin 4, Ireland
| | - Weston B Struwe
- National Institute for Bioprocessing Research and Training (NIBRT), Fosters Avenue, Dublin 4, Ireland
| | | | - Han Kok
- Merck, Sharp & Dohme, 5340 BH Oss, The Netherlands
| | | | - Pauline M Rudd
- National Institute for Bioprocessing Research and Training (NIBRT), Fosters Avenue, Dublin 4, Ireland
| | - Keith F Tipton
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
| | - Gavin P Davey
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
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47
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Spahn PN, Lewis NE. Systems glycobiology for glycoengineering. Curr Opin Biotechnol 2014; 30:218-24. [PMID: 25202878 DOI: 10.1016/j.copbio.2014.08.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 08/14/2014] [Accepted: 08/15/2014] [Indexed: 12/21/2022]
Abstract
Glycosylation serves essential functions on many proteins produced in biopharmaceutical manufacturing, making it mandatory to thoroughly consider its biogenesis during the production process. Glycoengineering efforts involve the rational design of glycosylation through adjustments in culturing conditions or genetic modifications. Computational models have been developed to aid this process, aiming to offer cheaper and faster alternatives to costly screening strategies. Recently, these models have been successfully utilized to predict glycosylation of products of industrial relevance. Furthermore, systems-level analyses of glycan diversity are elucidating deeper insights into the mechanisms underlying glycosylation. As computational models of glycosylation continue to be expanded, refined, and leveraged for detailed analysis of glycomics data, they will become invaluable resources for cell line development and glycoengineering.
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Affiliation(s)
- Philipp N Spahn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, United States
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, United States.
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48
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Liu G, Neelamegham S. A computational framework for the automated construction of glycosylation reaction networks. PLoS One 2014; 9:e100939. [PMID: 24978019 PMCID: PMC4076241 DOI: 10.1371/journal.pone.0100939] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 06/02/2014] [Indexed: 11/18/2022] Open
Abstract
Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS) data. The features described above are illustrated using three case studies that examine: i) O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii) automated N-linked glycosylation pathway construction; and iii) the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme biochemistry. All the implemented features are provided as part of the Glycosylation Network Analysis Toolbox (GNAT), an open-source, platform-independent, MATLAB based toolbox for studies of Systems Glycobiology.
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Affiliation(s)
- Gang Liu
- Department of Chemical and Biological Engineering, and The NY State Center for Excellence in Bioinformatics and Life Sciences, State University of New York, Buffalo, New York, United States of America
| | - Sriram Neelamegham
- Department of Chemical and Biological Engineering, and The NY State Center for Excellence in Bioinformatics and Life Sciences, State University of New York, Buffalo, New York, United States of America
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49
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Almquist J, Cvijovic M, Hatzimanikatis V, Nielsen J, Jirstrand M. Kinetic models in industrial biotechnology - Improving cell factory performance. Metab Eng 2014; 24:38-60. [PMID: 24747045 DOI: 10.1016/j.ymben.2014.03.007] [Citation(s) in RCA: 158] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Revised: 03/07/2014] [Accepted: 03/09/2014] [Indexed: 11/16/2022]
Abstract
An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.
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Affiliation(s)
- Joachim Almquist
- Fraunhofer-Chalmers Centre, Chalmers Science Park, SE-412 88 Göteborg, Sweden; Systems and Synthetic Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden.
| | - Marija Cvijovic
- Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-412 96 Göteborg, Sweden; Mathematical Sciences, University of Gothenburg, SE-412 96 Göteborg, Sweden
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne, CH 1015 Lausanne, Switzerland
| | - Jens Nielsen
- Systems and Synthetic Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden
| | - Mats Jirstrand
- Fraunhofer-Chalmers Centre, Chalmers Science Park, SE-412 88 Göteborg, Sweden
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50
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Controllability analysis of protein glycosylation in CHO cells. PLoS One 2014; 9:e87973. [PMID: 24498415 PMCID: PMC3912168 DOI: 10.1371/journal.pone.0087973] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Accepted: 01/02/2014] [Indexed: 12/17/2022] Open
Abstract
To function as intended in vivo, a majority of biopharmaceuticals require specific glycan distributions. However, achieving a precise glycan distribution during manufacturing can be challenging because glycosylation is a non-template driven cellular process, with the potential for significant uncontrolled variability in glycan distributions. As important as the glycan distribution is to the end-use performance of biopharmaceuticals, to date, no strategy exists for controlling glycosylation on-line. However, before expending the significant amount of effort and expense required to develop and implement on-line control strategies to address the problem of glycosylation heterogeneity, it is imperative to assess first the extent to which the very complex process of glycosylation is controllable, thereby establishing what is theoretically achievable prior to any experimental attempts. In this work, we present a novel methodology for assessing the output controllability of glycosylation, a prototypical example of an extremely high-dimensional and very non-linear system. We first discuss a method for obtaining the process gain matrix for glycosylation that involves performing model simulations and data analysis systematically and judiciously according to a statistical design of experiments (DOE) scheme and then employing Analysis of Variance (ANOVA) to determine the elements of process gain matrix from the resulting simulation data. We then discuss how to use the resulting high-dimensional gain matrix to assess controllability. The utility of this method is demonstrated with a practical example where we assess the controllability of various classes of glycans and of specific glycoforms that are typically found in recombinant biologics produced with Chinese Hamster Ovary (CHO) cells. In addition to providing useful insight into the extent to which on-line glycosylation control is achievable in actual manufacturing processes, the results also have important implications for genetically engineering cell lines design for enhanced glycosylation controllability.
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