1
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Yom A, Chiang A, Lewis NE. Boltzmann Model Predicts Glycan Structures from Lectin Binding. Anal Chem 2024; 96:8332-8341. [PMID: 38720429 PMCID: PMC11162346 DOI: 10.1021/acs.analchem.3c04992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2024]
Abstract
Glycans are complex oligosaccharides that are involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese hamster ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.
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Affiliation(s)
- Aria Yom
- Department of Physics, University of California, San Diego, California 92093, United States
| | - Austin Chiang
- Department of Pediatrics, University of California, San Diego, California 92093, United States
- Immunology Center of Georgia, Augusta University, Augusta, Georgia 30912, United States
- Department of Medicine, Augusta University, Augusta, Georgia 30912, United States
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, California 92093, United States
- Department of Bioengineering, University of California, San Diego, California 92093, United States
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2
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Li H, Peralta AG, Schoffelen S, Hansen AH, Arnsdorf J, Schinn SM, Skidmore J, Choudhury B, Paulchakrabarti M, Voldborg BG, Chiang AW, Lewis NE. LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587044. [PMID: 38585977 PMCID: PMC10996628 DOI: 10.1101/2024.03.27.587044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.
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Affiliation(s)
- Haining Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Angelo G. Peralta
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sanne Schoffelen
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Anders Holmgaard Hansen
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Johnny Arnsdorf
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Song-Min Schinn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan Skidmore
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA
| | - Biswa Choudhury
- Glycobiology Research and Training Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mousumi Paulchakrabarti
- Glycobiology Research and Training Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bjorn G. Voldborg
- National Biologics Facility Department of Biotechnology and Biomedicine, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby Denmark
| | - Austin W.T. Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E. Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
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3
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Yom A, Chiang A, Lewis NE. A Boltzmann model predicts glycan structures from lectin binding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.03.543532. [PMID: 37333412 PMCID: PMC10274649 DOI: 10.1101/2023.06.03.543532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Glycans are complex oligosaccharides involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese Hamster Ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.
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Affiliation(s)
- Aria Yom
- Department of Physics, University of California, San Diego. CA 92093, USA
| | - Austin Chiang
- Department of Pediatrics, University of California, San Diego. CA 92093, USA
- Immunology Center of Georgia, Augusta University, Augusta, GA 30912, USA
- Department of Medicine, Augusta University, Augusta, GA 30912, USA
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego. CA 92093, USA
- Department of Pediatrics, University of California, San Diego. CA 92093, USA
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4
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Liang C, Murray S, Li Y, Lee R, Low A, Sasaki S, Chiang AWT, Lin WJ, Mathews J, Barnes W, Lewis NE. LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework. Metab Eng 2024; 82:110-122. [PMID: 38311182 PMCID: PMC11163374 DOI: 10.1016/j.ymben.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/03/2024] [Accepted: 01/21/2024] [Indexed: 02/10/2024]
Abstract
Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.
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Affiliation(s)
- Chenguang Liang
- Department of Bioengineering, University of California, La Jolla, CA, 92093, USA
| | - Sue Murray
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Yang Li
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Richard Lee
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Audrey Low
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Shruti Sasaki
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, La Jolla, CA, 92093, USA
| | - Wen-Jen Lin
- Graduate Institute of Biomedical Science, China Medical University, Taichung 404333, Taiwan
| | - Joel Mathews
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Will Barnes
- Ionis Pharmaceuticals, Inc., Carlsbad, CA, 92010, USA
| | - Nathan E Lewis
- Department of Bioengineering, University of California, La Jolla, CA, 92093, USA; Department of Pediatrics, University of California, La Jolla, CA, 92093, USA.
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5
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Cheng W, Fang X, Guan Z, Yao Y, Xu Z, Bi Y, Ren K, Li J, Chen F, Chen X, Ma W, Chu Z, Deng Z, Zhang Z, Lu L. Functional characterization and structural basis of a reversible glycosyltransferase involves in plant chemical defence. PLANT BIOTECHNOLOGY JOURNAL 2023; 21:2611-2624. [PMID: 37581303 PMCID: PMC10651139 DOI: 10.1111/pbi.14157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/22/2023] [Accepted: 07/27/2023] [Indexed: 08/16/2023]
Abstract
Plants experience numerous biotic stresses throughout their lifespan, such as pathogens and pests, which can substantially affect crop production. In response, plants have evolved various metabolites that help them withstand these stresses. Here, we show that two specialized metabolites in the herbaceous perennial Belamcanda chinensis, tectorigenin and its glycoside tectoridin, have diverse defensive effects against phytopathogenic microorganisms and antifeeding effects against insect pest. We further functionally characterized a 7-O-uridine diphosphate glycosyltransferase Bc7OUGT, which catalyses a novel reversible glycosylation of tectorigenin and tectoridin. To elucidate the catalytic mechanisms of Bc7OUGT, we solved its crystal structure in complex with UDP and UDP/tectorigenin respectively. Structural analysis revealed the Bc7OUGT possesses a narrow but novel substrate-binding pocket made up by plentiful aromatic residues. Further structure-guided mutagenesis of these residues increased both glycosylation and deglycosylation activities. The catalytic reversibility of Bc7OUGT was also successfully applied in an one-pot aglycon exchange reaction. Our findings demonstrated the promising biopesticide activity of tectorigenin and its glycosides, and the characterization and mechanistic study of Bc7OUGT could facilitate the design of novel reversible UGTs to produce valuable glycosides with health benefits for both plants and humans.
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Affiliation(s)
- Weijia Cheng
- Department of Integrated Traditional Chinese Medicine and Western MedicineZhongnan Hospital of Wuhan University, School of Pharmaceutical SciencesWuhan UniversityWuhanChina
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Xueting Fang
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Zhifeng Guan
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Yan Yao
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Zhenni Xu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Yunya Bi
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Kexin Ren
- State Key Laboratory of Hybrid Rice, Department of Genetics, College of Life SciencesWuhan UniversityWuhanChina
| | - Jiwen Li
- Hubei Insect Resources Utilization and Sustainable Pest Management Key Laboratory, College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Fangfang Chen
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Xiangsong Chen
- State Key Laboratory of Hybrid Rice, Department of Genetics, College of Life SciencesWuhan UniversityWuhanChina
- Hubei Hongshan LaboratoryWuhanChina
| | - Weihua Ma
- Hubei Insect Resources Utilization and Sustainable Pest Management Key Laboratory, College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
- Hubei Hongshan LaboratoryWuhanChina
| | - Zhaohui Chu
- State Key Laboratory of Hybrid Rice, Department of Genetics, College of Life SciencesWuhan UniversityWuhanChina
- Hubei Hongshan LaboratoryWuhanChina
| | - Zixin Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Zhengyu Zhang
- Department of Integrated Traditional Chinese Medicine and Western MedicineZhongnan Hospital of Wuhan University, School of Pharmaceutical SciencesWuhan UniversityWuhanChina
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
| | - Li Lu
- Department of Integrated Traditional Chinese Medicine and Western MedicineZhongnan Hospital of Wuhan University, School of Pharmaceutical SciencesWuhan UniversityWuhanChina
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical SciencesWuhan UniversityWuhanChina
- Hubei Hongshan LaboratoryWuhanChina
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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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Rocamora F, Peralta AG, Shin S, Sorrentino J, Wu MYM, Toth EA, Fuerst TR, Lewis NE. Glycosylation shapes the efficacy and safety of diverse protein, gene and cell therapies. Biotechnol Adv 2023; 67:108206. [PMID: 37354999 PMCID: PMC11168894 DOI: 10.1016/j.biotechadv.2023.108206] [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: 03/02/2023] [Revised: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 06/26/2023]
Abstract
Over recent decades, therapeutic proteins have had widespread success in treating a myriad of diseases. Glycosylation, a near universal feature of this class of drugs, is a critical quality attribute that significantly influences the physical properties, safety profile and biological activity of therapeutic proteins. Optimizing protein glycosylation, therefore, offers an important avenue to developing more efficacious therapies. In this review, we discuss specific examples of how variations in glycan structure and glycoengineering impacts the stability, safety, and clinical efficacy of protein-based drugs that are already in the market as well as those that are still in preclinical development. We also highlight the impact of glycosylation on next generation biologics such as T cell-based cancer therapy and gene therapy.
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Affiliation(s)
- Frances Rocamora
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Angelo G Peralta
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Seunghyeon Shin
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - James Sorrentino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mina Ying Min Wu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Eric A Toth
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
| | - Thomas R Fuerst
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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Liang C, Chiang AWT, Lewis NE. GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data. STAR Protoc 2023; 4:102244. [PMID: 37086409 PMCID: PMC10160804 DOI: 10.1016/j.xpro.2023.102244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/07/2023] [Accepted: 03/24/2023] [Indexed: 04/23/2023] Open
Abstract
Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We describe steps for annotating glycomics data and quantifying perturbations to N-glycan biosynthesis with interpretable models. We then detail procedures to predict the impact of mutations in disease or potential glycoengineering strategies in drug development. For complete details on the use and execution of this protocol, please refer to Liang et al. (2020).1.
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Affiliation(s)
- Chenguang Liang
- Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA; Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92130, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, San Diego, CA 92130, USA; Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92130, USA.
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Kotidis P, Donini R, Arnsdorf J, Hansen AH, Voldborg BGR, Chiang AWT, Haslam SM, Betenbaugh M, Jimenez Del Val I, Lewis NE, Krambeck F, Kontoravdi C. CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells. Metab Eng 2023; 76:87-96. [PMID: 36610518 PMCID: PMC11132536 DOI: 10.1016/j.ymben.2022.12.009] [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: 03/07/2022] [Revised: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023]
Abstract
Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.
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Affiliation(s)
- Pavlos Kotidis
- Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK.
| | - Roberto Donini
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Johnny Arnsdorf
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Anders Holmgaard Hansen
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Bjørn Gunnar Rude Voldborg
- National Biologics Facility, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA
| | - Stuart M Haslam
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Michael Betenbaugh
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, CA, 92093, USA; Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | | | - Cleo Kontoravdi
- Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK.
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Groth T, Diehl AD, Gunawan R, Neelamegham S. GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology. Bioinformatics 2022; 38:5413-5420. [PMID: 36282863 PMCID: PMC9750110 DOI: 10.1093/bioinformatics/btac704] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/22/2022] [Accepted: 10/24/2022] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION The 'glycoEnzymes' include a set of proteins having related enzymatic, metabolic, transport, structural and cofactor functions. Currently, there is no established ontology to describe glycoEnzyme properties and to relate them to glycan biosynthesis pathways. RESULTS We present GlycoEnzOnto, an ontology describing 403 human glycoEnzymes curated along 139 glycosylation pathways, 134 molecular functions and 22 cellular compartments. The pathways described regulate nucleotide-sugar metabolism, glycosyl-substrate/donor transport, glycan biosynthesis and degradation. The role of each enzyme in the glycosylation initiation, elongation/branching and capping/termination phases is described. IUPAC linear strings present systematic human/machine-readable descriptions of individual reaction steps and enable automated knowledge-based curation of biochemical networks. All GlycoEnzOnto knowledge is integrated with the Gene Ontology biological processes. GlycoEnzOnto enables improved transcript overrepresentation analyses and glycosylation pathway identification compared to other available schema, e.g. KEGG and Reactome. Overall, GlycoEnzOnto represents a holistic glycoinformatics resource for systems-level analyses. AVAILABILITY AND IMPLEMENTATION https://github.com/neel-lab/GlycoEnzOnto. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Theodore Groth
- Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
| | - Sriram Neelamegham
- Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
- Department of Medicine, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
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11
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A roadmap for translational cancer glycoimmunology at single cell resolution. J Exp Clin Cancer Res 2022; 41:143. [PMID: 35428302 PMCID: PMC9013178 DOI: 10.1186/s13046-022-02335-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 03/17/2022] [Indexed: 11/11/2022] Open
Abstract
Cancer cells can evade immune responses by exploiting inhibitory immune checkpoints. Immune checkpoint inhibitor (ICI) therapies based on anti-CTLA-4 and anti-PD-1/PD-L1 antibodies have been extensively explored over the recent years to unleash otherwise compromised anti-cancer immune responses. However, it is also well established that immune suppression is a multifactorial process involving an intricate crosstalk between cancer cells and the immune systems. The cancer glycome is emerging as a relevant source of immune checkpoints governing immunosuppressive behaviour in immune cells, paving an avenue for novel immunotherapeutic options. This review addresses the current state-of-the-art concerning the role played by glycans controlling innate and adaptive immune responses, while shedding light on available experimental models for glycoimmunology. We also emphasize the tremendous progress observed in the development of humanized models for immunology, the paramount contribution of advances in high-throughput single-cell analysis in this context, and the importance of including predictive machine learning algorithms in translational research. This may constitute an important roadmap for glycoimmunology, supporting careful adoption of models foreseeing clinical translation of fundamental glycobiology knowledge towards next generation immunotherapies.
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12
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Li H, Chiang AWT, Lewis NE. Artificial intelligence in the analysis of glycosylation data. Biotechnol Adv 2022; 60:108008. [PMID: 35738510 PMCID: PMC11157671 DOI: 10.1016/j.biotechadv.2022.108008] [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: 01/08/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022]
Abstract
Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery.
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Affiliation(s)
- Haining Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
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13
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Puranik A, Saldanha M, Chirmule N, Dandekar P, Jain R. Advanced strategies in glycosylation prediction and control during biopharmaceutical development: Avenues toward Industry 4.0. Biotechnol Prog 2022; 38:e3283. [PMID: 35752935 DOI: 10.1002/btpr.3283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/31/2022] [Accepted: 06/17/2022] [Indexed: 11/09/2022]
Abstract
Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multi-variate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm. Nonetheless, the development of smart algorithms and omics-enabled complete cell characterization have made it possible to predict glycan profiles beforehand, and manipulate process variables accordingly. This review thus discusses the various approaches available for control and prediction of glycosylation in biopharmaceuticals. Further, the futuristic goal of integrating such technologies is discussed in order to attain an automated and digitized continuous bioprocess for control of glycosylation. Given, control of a process as complex as glycosylation requires intense monitoring intervention, we examine the current technologies that enable automation. Finally, we discuss the challenges and the technological gap that currently limits incorporation of an automated process in routine bio-manufacturing, with a glimpse into the economic bearing. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Amita Puranik
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
| | - Marianne Saldanha
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
| | | | - Prajakta Dandekar
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Matunga, Mumbai, India
| | - Ratnesh Jain
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
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14
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Flevaris K, Kontoravdi C. Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective. Int J Mol Sci 2022; 23:ijms23095180. [PMID: 35563570 PMCID: PMC9100869 DOI: 10.3390/ijms23095180] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 02/04/2023] Open
Abstract
The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed.
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15
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Kellman BP, Richelle A, Yang JY, Chapla D, Chiang AWT, Najera JA, Liang C, Fürst A, Bao B, Koga N, Mohammad MA, Bruntse AB, Haymond MW, Moremen KW, Bode L, Lewis NE. Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration. Nat Commun 2022; 13:2455. [PMID: 35508452 PMCID: PMC9068700 DOI: 10.1038/s41467-022-29867-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 04/04/2022] [Indexed: 12/18/2022] Open
Abstract
Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development. Human milk oligosaccharides are fundamental to infant health. Here the authors deploy a multi-omics systems biology approach to elucidate their biosynthetic network, including the associated enzymes and likely structures of ambiguous oligosaccharides.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jeong-Yeh Yang
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Digantkumar Chapla
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Julia A Najera
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Chenguang Liang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Annalee Fürst
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Natalia Koga
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Mahmoud A Mohammad
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Anders Bech Bruntse
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Morey W Haymond
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kelley W Moremen
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Lars Bode
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.,Larsson-Rosenquist Foundation Mother-Milk-Infant Center of Research Excellence (MOMI CORE), University of California, San Diego, La Jolla, CA, 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA. .,Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
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16
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Walsh I, Myint M, Nguyen-Khuong T, Ho YS, Ng SK, Lakshmanan M. Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing. MAbs 2022; 14:2013593. [PMID: 35000555 PMCID: PMC8744891 DOI: 10.1080/19420862.2021.2013593] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.
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Affiliation(s)
- Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Matthew Myint
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Terry Nguyen-Khuong
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Say Kong Ng
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.,Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore
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17
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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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18
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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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19
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Luo Y, Kurian V, Ogunnaike BA. Bioprocess systems analysis, modeling, estimation, and control. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100705] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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20
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Savizi ISP, Motamedian E, E Lewis N, Jimenez Del Val I, Shojaosadati SA. An integrated modular framework for modeling the effect of ammonium on the sialylation process of monoclonal antibodies produced by CHO cells. Biotechnol J 2021; 16:e2100019. [PMID: 34021707 DOI: 10.1002/biot.202100019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Monoclonal antibodies (mABs) have emerged as one of the most important therapeutic recombinant proteins in the pharmaceutical industry. Their immunogenicity and therapeutic efficacy are influenced by post-translational modifications, specifically the glycosylation process. Bioprocess conditions can influence the intracellular process of glycosylation. Among all the process conditions that have been recognized to affect the mAB glycoforms, the detailed mechanism underlying how ammonium could perturb glycosylation remains to be fully understood. It was shown that ammonium induces heterogeneity in protein glycosylation by altering the sialic acid content of glycoproteins. Hence, understanding this mechanism would aid pharmaceutical manufacturers to ensure consistent protein glycosylation. METHODS Three different mechanisms have been proposed to explain how ammonium influences the sialylation process. In the first, the inhibition of CMP-sialic acid transporter, which transports CMP-sialic acid (sialylation substrate) into the Golgi, by an increase in UDP-GlcNAc content that is brought about by the augmented incorporation of ammonium into glucosamine formation. In the second, ammonia diffuses into the Golgi and raises its pH, thereby decreasing the sialyltransferase enzyme activity. In the third, the reduction of sialyltransferase enzyme expression level in the presence of ammonium. We employed these mechanisms in a novel integrated modular platform to link dynamic alteration in mAB sialylation process with extracellular ammonium concentration to elucidate how ammonium alters the sialic acid content of glycoproteins. RESULTS Our results show that the sialylation reaction rate is insensitive to the first mechanism. At low ammonium concentration, the second mechanism is the controlling mechanism in mAB sialylation and by increasing the ammonium level (< 8 mM) the third mechanism becomes the controlling mechanism. At higher ammonium concentrations (> 8 mM) the second mechanism becomes predominant again. CONCLUSION The presented model in this study provides a connection between extracellular ammonium and the monoclonal antibody sialylation process. This computational tool could help scientists to develop and formulate cell culture media. The model illustrated here can assist the researchers to select culture media that ensure consistent mAB sialylation.
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Affiliation(s)
- Iman Shahidi Pour Savizi
- Faculty of Chemical Engineering, Biotechnology Department, Tarbiat Modares University, Tehran, Iran
| | - Ehsan Motamedian
- Faculty of Chemical Engineering, Biotechnology Department, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, La Jolla, California, USA.,School of Medicine, Novo Nordisk Foundation Center for Biosustainability at the University of California, La Jolla, California, USA.,Department of Pediatrics, School of Medicine, University of California, La Jolla, California, USA
| | | | - Seyed Abbas Shojaosadati
- Faculty of Chemical Engineering, Biotechnology Department, Tarbiat Modares University, Tehran, Iran
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21
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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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22
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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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23
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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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24
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Zhang L, Wang M, Castan A, Hjalmarsson H, Chotteau V. Probabilistic model by Bayesian network for the prediction of antibody glycosylation in perfusion and fed-batch cell cultures. Biotechnol Bioeng 2021; 118:3447-3459. [PMID: 33788254 DOI: 10.1002/bit.27769] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 01/01/2023]
Abstract
Glycosylation is a critical quality attribute of therapeutic monoclonal antibodies (mAbs). The glycan pattern can have a large impact on the immunological functions, serum half-life and stability. The medium components and cultivation parameters are known to potentially influence the glycosylation profile. Mathematical modelling provides a strategy for rational design and control of the upstream bioprocess. However, the kinetic models usually contain a very large number of unknown parameters, which limit their practical applications. In this article, we consider the metabolic network of N-linked glycosylation as a Bayesian network (BN) and calculate the fluxes of the glycosylation process as joint probability using the culture parameters as inputs. The modelling approach is validated with data of different Chinese hamster ovary cell cultures in pseudo perfusion, perfusion, and fed batch cultures, all showing very good predictive capacities. In cases where a large number of cultivation parameters is available, it is shown here that principal components analysis can efficiently be employed for a dimension reduction of the inputs compared to Pearson correlation analysis and feature importance by decision tree. The present study demonstrates that BN model can be a powerful tool in upstream process and medium development for glycoprotein productions.
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Affiliation(s)
- Liang Zhang
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden
| | - MingLiang Wang
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | | | - Håkan Hjalmarsson
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Stockholm, Sweden.,Digital Futures - KTH Royal Institute of Technology, Stockholm, Sweden
| | - Veronique Chotteau
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Digital Futures - KTH Royal Institute of Technology, Stockholm, Sweden
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25
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Bryan L, Clynes M, Meleady P. The emerging role of cellular post-translational modifications in modulating growth and productivity of recombinant Chinese hamster ovary cells. Biotechnol Adv 2021; 49:107757. [PMID: 33895332 DOI: 10.1016/j.biotechadv.2021.107757] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 02/06/2023]
Abstract
Chinese hamster ovary (CHO) cells are one of the most commonly used host cell lines used for the production human therapeutic proteins. Much research over the past two decades has focussed on improving the growth, titre and cell specific productivity of CHO cells and in turn lowering the costs associated with production of recombinant proteins. CHO cell engineering has become of particular interest in recent years following the publication of the CHO cell genome and the availability of data relating to the proteome, transcriptome and metabolome of CHO cells. However, data relating to the cellular post-translational modification (PTMs) which can affect the functionality of CHO cellular proteins has only begun to be presented in recent years. PTMs are important to many cellular processes and can further alter proteins by increasing the complexity of proteins and their interactions. In this review, we describe the research presented from CHO cells to date related on three of the most important PTMs; glycosylation, phosphorylation and ubiquitination.
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Affiliation(s)
- Laura Bryan
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Martin Clynes
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Paula Meleady
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland.
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26
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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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27
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Kellman BP, Lewis NE. Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication. Trends Biochem Sci 2021; 46:284-300. [PMID: 33349503 PMCID: PMC7954846 DOI: 10.1016/j.tibs.2020.10.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 10/05/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022]
Abstract
Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability at the University of California San Diego School of Medicine, La Jolla, CA, USA.
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28
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Promiscuity and specificity of eukaryotic glycosyltransferases. Biochem Soc Trans 2021; 48:891-900. [PMID: 32539082 PMCID: PMC7329348 DOI: 10.1042/bst20190651] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/18/2020] [Accepted: 05/20/2020] [Indexed: 02/07/2023]
Abstract
Glycosyltransferases are a large family of enzymes responsible for covalently linking sugar monosaccharides to a variety of organic substrates. These enzymes drive the synthesis of complex oligosaccharides known as glycans, which play key roles in inter-cellular interactions across all the kingdoms of life; they also catalyze sugar attachment during the synthesis of small-molecule metabolites such as plant flavonoids. A given glycosyltransferase enzyme is typically responsible for attaching a specific donor monosaccharide, via a specific glycosidic linkage, to a specific moiety on the acceptor substrate. However these enzymes are often promiscuous, able catalyze linkages between a variety of donors and acceptors. In this review we discuss distinct classes of glycosyltransferase promiscuity, each illustrated by enzymatic examples from small-molecule or glycan synthesis. We highlight the physical causes of promiscuity, and its biochemical consequences. Structural studies of glycosyltransferases involved in glycan synthesis show that they make specific contacts with ‘recognition motifs’ that are much smaller than the full oligosaccharide substrate. There is a wide range in the sizes of glycosyltransferase recognition motifs: highly promiscuous enzymes recognize monosaccharide or disaccharide motifs across multiple oligosaccharides, while highly specific enzymes recognize large, complex motifs found on few oligosaccharides. In eukaryotes, the localization of glycosyltransferases within compartments of the Golgi apparatus may play a role in mitigating the glycan variability caused by enzyme promiscuity.
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29
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Bojar D, Powers RK, Camacho DM, Collins JJ. Deep-Learning Resources for Studying Glycan-Mediated Host-Microbe Interactions. Cell Host Microbe 2021; 29:132-144.e3. [DOI: 10.1016/j.chom.2020.10.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/09/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
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30
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Zhang L, Schwarz H, Wang M, Castan A, Hjalmarsson H, Chotteau V. Control of IgG glycosylation in CHO cell perfusion cultures by GReBA mathematical model supported by a novel targeted feed, TAFE. Metab Eng 2020; 65:135-145. [PMID: 33161144 DOI: 10.1016/j.ymben.2020.11.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/15/2020] [Accepted: 11/02/2020] [Indexed: 10/23/2022]
Abstract
The N-linked glycosylation pattern is an important quality attribute of therapeutic glycoproteins. It has been reported by our group and by others that different carbon sources, such as glucose, mannose and galactose, can differently impact the glycosylation profile of glycoproteins in mammalian cell culture. Acting on the sugar feeding is thus an attractive strategy to tune the glycan pattern. However, in case of feeding of more than one carbon source simultaneously, the cells give priority to the one with the highest uptake rate, which limits the usage of this tuning, e.g. the cells favor consuming glucose in comparison to galactose. We present here a new feeding strategy (named 'TAFE' for targeted feeding) for perfusion culture to adjust the concentrations of fed sugars influencing the glycosylation. The strategy consists in setting the sugar feeding such that the cells are forced to consume these substrates at a target cell specific consumption rate decided by the operator and taking into account the cell specific perfusion rate (CSPR). This strategy is applied in perfusion cultures of Chinese hamster ovary (CHO) cells, illustrated by ten different regimes of sugar feeding, including glucose, galactose and mannose. Applying the TAFE strategy, different glycan profiles were obtained using the different feeding regimes. Furthermore, we successfully forced the cells to consume higher proportions of non-glucose sugars, which have lower transport rates than glucose in presence of this latter, in a controlled way. In previous work, a mathematical model named Glycan Residues Balance Analysis (GReBA) was developed to model the glycosylation profile based on the fed carbon sources. The present data were applied to the GReBA to design a feeding regime targeting a given glycosylation profile. The ability of the model to achieve this objective was confirmed by a multi-round of leave-one-out cross-validation (LOOCV), leading to the conclusion that the GReBA model can be used to design the feeding regime of a perfusion cell culture to obtain a desired glycosylation profile.
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Affiliation(s)
- Liang Zhang
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden
| | - Hubert Schwarz
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden
| | - Mingliang Wang
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden; Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Sweden
| | | | - Håkan Hjalmarsson
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden; Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Sweden
| | - Veronique Chotteau
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Sweden; AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH, Sweden.
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31
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Kellman BP, Zhang Y, Logomasini E, Meinhardt E, Godinez-Macias KP, Chiang AWT, Sorrentino JT, Liang C, Bao B, Zhou Y, Akase S, Sogabe I, Kouka T, Winzeler EA, Wilson IBH, Campbell MP, Neelamegham S, Krambeck FJ, Aoki-Kinoshita KF, Lewis NE. A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR). Beilstein J Org Chem 2020; 16:2645-2662. [PMID: 33178355 PMCID: PMC7607430 DOI: 10.3762/bjoc.16.215] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 09/17/2020] [Indexed: 12/18/2022] Open
Abstract
Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Here, we present Linear Code for Reaction Rules (LiCoRR), version 1.0, an unambiguous representation for describing glycosylation reactions in both literature and code.
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32
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Luo Y, Lovelett RJ, Price JV, Radhakrishnan D, Barnthouse K, Hu P, Schaefer E, Cunningham J, Lee KH, Shivappa RB, Ogunnaike BA. Modeling the Effect of Amino Acids and Copper on Monoclonal Antibody Productivity and Glycosylation: A Modular Approach. Biotechnol J 2020; 16:e2000261. [PMID: 32875683 DOI: 10.1002/biot.202000261] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/22/2020] [Indexed: 01/15/2023]
Abstract
In manufacturing monoclonal antibodies (mAbs), it is crucial to be able to predict how process conditions and supplements affect productivity and quality attributes, especially glycosylation. Supplemental inputs, such as amino acids and trace metals in the media, are reported to affect cell metabolism and glycosylation; quantifying their effects is essential for effective process development. We aim to present and validate, through a commercially relevant cell culture process, a technique for modeling such effects efficiently. While existing models can predict mAb production or glycosylation dynamics under specific process configurations, adapting them to new processes remains challenging, because it involves modifying the model structure and often requires some mechanistic understanding. Here, a modular modeling technique for adapting an existing model for a fed-batch Chinese hamster ovary (CHO) cell culture process without structural modifications or mechanistic insight is presented. Instead, data is used, obtained from designed experimental perturbations in media supplementation, to train and validate a supplemental input effect model, which is used to "patch" the existing model. The combined model can be used for model-based process development to improve productivity and to meet product quality targets more efficiently. The methodology and analysis are generally applicable to other CHO cell lines and cell types.
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Affiliation(s)
- Yu Luo
- University of Delaware, Chemical and Biomolecular Engineering, 150 Academy St, Newark, DE, 19716, USA
| | - Robert J Lovelett
- University of Delaware, Chemical and Biomolecular Engineering, 150 Academy St, Newark, DE, 19716, USA
| | - J Vincent Price
- Janssen Research and Development, Discovery, Product Development and Supply, 200 Great Valley Parkway, Malvern, PA, 19355, USA
| | - Devesh Radhakrishnan
- University of Delaware, Chemical and Biomolecular Engineering, 150 Academy St, Newark, DE, 19716, USA
| | - Kristopher Barnthouse
- Janssen Research and Development, Discovery, Product Development and Supply, 200 Great Valley Parkway, Malvern, PA, 19355, USA
| | - Ping Hu
- Janssen Research and Development, Discovery, Product Development and Supply, 200 Great Valley Parkway, Malvern, PA, 19355, USA
| | - Eugene Schaefer
- Janssen Research and Development, Discovery, Product Development and Supply, 200 Great Valley Parkway, Malvern, PA, 19355, USA
| | - John Cunningham
- Janssen Research and Development, Discovery, Product Development and Supply, 200 Great Valley Parkway, Malvern, PA, 19355, USA
| | - Kelvin H Lee
- University of Delaware, Chemical and Biomolecular Engineering, 150 Academy St, Newark, DE, 19716, USA
| | - Raghunath B Shivappa
- Takeda Pharmaceuticals, Biologics Process Development, 200 Shire Way, Lexington, MA, 02421, USA
| | - Babatunde A Ogunnaike
- University of Delaware, Chemical and Biomolecular Engineering, 150 Academy St, Newark, DE, 19716, USA
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33
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Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review. Processes (Basel) 2020. [DOI: 10.3390/pr8091088] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.
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34
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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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35
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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: 39] [Impact Index Per Article: 9.8] [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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36
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Jaiman A, Thattai M. Golgi compartments enable controlled biomolecular assembly using promiscuous enzymes. eLife 2020; 9:49573. [PMID: 32597757 PMCID: PMC7360365 DOI: 10.7554/elife.49573] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 06/28/2020] [Indexed: 12/31/2022] Open
Abstract
The synthesis of eukaryotic glycans - branched sugar oligomers attached to cell-surface proteins and lipids - is organized like a factory assembly line. Specific enzymes within successive compartments of the Golgi apparatus determine where new monomer building blocks are linked to the growing oligomer. These enzymes act promiscuously and stochastically, causing microheterogeneity (molecule-to-molecule variability) in the final oligomer products. However, this variability is tightly controlled: a given eukaryotic protein type is typically associated with a narrow, specific glycan oligomer profile. Here, we use ideas from the mathematical theory of self-assembly to enumerate the enzymatic causes of oligomer variability and show how to eliminate each cause. We rigorously demonstrate that cells can specifically synthesize a larger repertoire of glycan oligomers by partitioning promiscuous enzymes across multiple Golgi compartments. This places limits on biomolecular assembly: glycan microheterogeneity becomes unavoidable when the number of compartments is limited, or enzymes are excessively promiscuous.
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Affiliation(s)
- Anjali Jaiman
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Mukund Thattai
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
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37
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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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38
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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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39
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Zürcher P, Sokolov M, Brühlmann D, Ducommun R, Stettler M, Souquet J, Jordan M, Broly H, Morbidelli M, Butté A. Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction. Biotechnol Prog 2020; 36:e3012. [DOI: 10.1002/btpr.3012] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 03/24/2020] [Accepted: 04/10/2020] [Indexed: 01/08/2023]
Affiliation(s)
- Philipp Zürcher
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
| | - Michael Sokolov
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
- DataHow AG Zurich Switzerland
| | - David Brühlmann
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Raphael Ducommun
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Matthieu Stettler
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Jonathan Souquet
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Martin Jordan
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Hervé Broly
- Merck Biopharma, Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Massimo Morbidelli
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
- DataHow AG Zurich Switzerland
| | - Alessandro Butté
- Department of Chemistry and Applied Biosciences Institute of Chemical and Bioengineering ETH Zürich Switzerland
- DataHow AG Zurich Switzerland
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40
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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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41
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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: 12] [Impact Index Per Article: 3.0] [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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42
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Gutierrez JM, Feizi A, Li S, Kallehauge TB, Hefzi H, Grav LM, Ley D, Baycin Hizal D, Betenbaugh MJ, Voldborg B, Faustrup Kildegaard H, Min Lee G, Palsson BO, Nielsen J, Lewis NE. Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion. Nat Commun 2020; 11:68. [PMID: 31896772 PMCID: PMC6940358 DOI: 10.1038/s41467-019-13867-y] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 11/22/2019] [Indexed: 01/08/2023] Open
Abstract
In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.
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Affiliation(s)
- Jahir M Gutierrez
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, 92093, USA
| | - Amir Feizi
- Department of Biology and Biological Engineering, Kemivägen 10, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Shangzhong Li
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, 92093, USA
| | - Thomas B Kallehauge
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Hooman Hefzi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, 92093, USA
| | - Lise M Grav
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Daniel Ley
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Michael J Betenbaugh
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218-2686, USA
| | - Bjorn Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Helene Faustrup Kildegaard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Gyun Min Lee
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, 92093, USA
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Kemivägen 10, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, 92093, USA.
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, 92093, USA.
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43
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Fisher P, Thomas-Oates J, Wood AJ, Ungar D. The N-Glycosylation Processing Potential of the Mammalian Golgi Apparatus. Front Cell Dev Biol 2019; 7:157. [PMID: 31457009 PMCID: PMC6700225 DOI: 10.3389/fcell.2019.00157] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 07/26/2019] [Indexed: 11/16/2022] Open
Abstract
Heterogeneity is an inherent feature of the glycosylation process. Mammalian cells often produce a variety of glycan structures on separate molecules of the same protein, known as glycoforms. This heterogeneity is not random but is controlled by the organization of the glycosylation machinery in the Golgi cisternae. In this work, we use a computational model of the N-glycosylation process to probe how the organization of the glycosylation machinery into different cisternae drives N-glycan biosynthesis toward differing degrees of heterogeneity. Using this model, we demonstrate the N-glycosylation potential and limits of the mammalian Golgi apparatus, for example how the number of cisternae limits the goal of achieving near homogeneity for N-glycans. The production of specific glycoforms guided by this computational study could pave the way for “glycoform engineering,” which will find uses in the functional investigation of glycans, the modulation of glycan-mediated physiological functions, and in biotechnology.
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Affiliation(s)
- Peter Fisher
- Department of Biology, University of York, York, United Kingdom
| | - Jane Thomas-Oates
- Department of Chemistry and Centre of Excellence in Mass Spectrometry, University of York, York, United Kingdom
| | - A Jamie Wood
- Department of Biology, University of York, York, United Kingdom.,Department of Mathematics, University of York, York, United Kingdom
| | - Daniel Ungar
- Department of Biology, University of York, York, United Kingdom
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Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019; 15:e1007084. [PMID: 31295267 PMCID: PMC6622478 DOI: 10.1371/journal.pcbi.1007084] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
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Affiliation(s)
- Guido Zampieri
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
- Healthcare Innovation Centre, Teesside University, Middlesbrough, United Kingdom
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45
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Kotidis P, Jedrzejewski P, Sou SN, Sellick C, Polizzi K, Del Val IJ, Kontoravdi C. Model-based optimization of antibody galactosylation in CHO cell culture. Biotechnol Bioeng 2019; 116:1612-1626. [PMID: 30802295 DOI: 10.1002/bit.26960] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/22/2019] [Accepted: 02/21/2019] [Indexed: 01/13/2023]
Abstract
Exerting control over the glycan moieties of antibody therapeutics is highly desirable from a product safety and batch-to-batch consistency perspective. Strategies to improve antibody productivity may compromise quality, while interventions for improving glycoform distribution can adversely affect cell growth and productivity. Process design therefore needs to consider the trade-off between preserving cellular health and productivity while enhancing antibody quality. In this work, we present a modeling platform that quantifies the impact of glycosylation precursor feeding - specifically that of galactose and uridine - on cellular growth, metabolism as well as antibody productivity and glycoform distribution. The platform has been parameterized using an initial training data set yielding an accuracy of ±5% with respect to glycoform distribution. It was then used to design an optimized feeding strategy that enhances the final concentration of galactosylated antibody in the supernatant by over 90% compared with the control without compromising the integral of viable cell density or final antibody titer. This work supports the implementation of Quality by Design towards higher-performing bioprocesses.
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Affiliation(s)
- Pavlos Kotidis
- Department of Chemical Engineering, Imperial College London, United Kingdom
| | - Philip Jedrzejewski
- Department of Chemical Engineering, Imperial College London, United Kingdom
- Department of Life Sciences, Imperial College London, United Kingdom
- Centre for Synthetic Biology and Innovation, Imperial College London, United Kingdom
| | - Si Nga Sou
- Department of Chemical Engineering, Imperial College London, United Kingdom
- Department of Life Sciences, Imperial College London, United Kingdom
- Centre for Synthetic Biology and Innovation, Imperial College London, United Kingdom
| | - Christopher Sellick
- Cell Culture and Fermentation Sciences BioPharmaceutical Development, MedImmune, Granta Park, Cambridge, United Kingdom
| | - Karen Polizzi
- Department of Life Sciences, Imperial College London, United Kingdom
- Centre for Synthetic Biology and Innovation, Imperial College London, United Kingdom
| | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, United Kingdom
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46
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Zhang L, Castan A, Stevenson J, Chatzissavidou N, Vilaplana F, Chotteau V. Combined effects of glycosylation precursors and lactate on the glycoprofile of IgG produced by CHO cells. J Biotechnol 2019; 289:71-79. [DOI: 10.1016/j.jbiotec.2018.11.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 10/30/2018] [Accepted: 11/06/2018] [Indexed: 12/29/2022]
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47
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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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48
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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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49
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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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50
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Blondeel EJM, Aucoin MG. Supplementing glycosylation: A review of applying nucleotide-sugar precursors to growth medium to affect therapeutic recombinant protein glycoform distributions. Biotechnol Adv 2018; 36:1505-1523. [PMID: 29913209 DOI: 10.1016/j.biotechadv.2018.06.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 05/10/2018] [Accepted: 06/13/2018] [Indexed: 01/02/2023]
Abstract
Glycosylation is a critical quality attribute (CQA) of many therapeutic proteins, particularly monoclonal antibodies (mAbs), and is a major consideration in the approval of biosimilar biologics due to its effects to therapeutic efficacy. Glycosylation generates a distribution of glycoforms, resulting in glycoproteins with inherent molecule-to-molecule heterogeneity, capable of activating (or failing to activate) different effector functions of the immune system. Glycoforms can be affected by the supplementation of nucleotide-sugar precursors, and related components, to culture growth medium, affecting the metabolism of glycosylation. These supplementations has been demonstrated to increase nucleotide-sugar intracellular pools, and impact glycoform distributions, but with varied results. These variations can be attributed to five key factors: Differences between cell platforms (enzyme/transporter expression levels); differences between recombinant proteins produced (glycan-site accessibility); the fermentation and sampling timeline (glucose availability and exoglycosidase accumulation); glutamine levels (affecting ammonia levels, which impact Golgi pH, as well as UDP-GlcNAc pools); and finally, a lack of standardized metrics for observing shifts in glycoform distributions (glycosylation indices) across different experiments. The purpose of this review is to provide detail and clarity on the state of the art of supplementation strategies for nucleotide-sugar precursors for affecting glycosylation in cell culture processes, and to apply glycosylation indices for standardized comparisons across the field.
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Affiliation(s)
- Eric J M Blondeel
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Marc G Aucoin
- Centre for Biotechnology and Bioengineering, Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
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