Model-based analysis of N-glycosylation in Chinese hamster ovary cells

In silico simulation of glycosylation and related pathways

Glycans participate in a vast number of recognition systems in diverse organisms in health and in disease. However, glycans cannot be sequenced because there is no sequencer technology that can fully characterize them. There is no "template" for replicating glycans as there are for amino acids and nucleic acids. Instead, glycans are synthesized by a complicated orchestration of multitudes of glycosyltransferases and glycosidases. Thus glycans can vary greatly in structure, but they are not genetically reproducible and are usually isolated in minute amounts. To characterize (sequence) the glycome (defined as the glycans in a particular organism, tissue, cell, or protein), glycosylation pathway prediction using in silico methods based on glycogene expression data, and glycosylation simulations have been attempted. Since many of the mammalian glycogenes have been identified and cloned, it has become possible to predict the glycan biosynthesis pathway in these systems. By then incorporating systems biology and bioprocessing technologies to these pathway models, given the right enzymatic parameters including enzyme and substrate concentrations and kinetic reaction parameters, it is possible to predict the potentially synthesized glycans in the pathway. This review presents information on the data resources that are currently available to enable in silico simulations of glycosylation and related pathways. Then some of the software tools that have been developed in the past to simulate and analyze glycosylation pathways will be described, followed by a summary and vision for the future developments and research directions in this area.

Glycosylation Modulation Dictates Trafficking and Interaction of SARS-CoV-2 S1 Subunit and ACE2 in Intestinal Epithelial Caco-2 Cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mainly targets the upper respiratory tract. It gains entry by interacting with the host cell receptor angiotensin-converting enzyme 2 (ACE2) via its heavily glycosylated spike glycoprotein. SARS-CoV-2 can also affect the gastrointestinal tract. Given the significant role of glycosylation in the life cycle of proteins and the multisystem target of SARS-CoV-2, the role of glycosylation in the interaction of S1 with ACE2 in Caco-2 cells was investigated after modulation of their glycosylation patterns using N-butyldeoxynojirimycin (NB-DNJ) and 1-deoxymannojirimycin (dMM), in addition to mutant CHO cells harboring mutations at different stages of glycosylation. The data show a substantial reduction in the interactions between the altered glycosylation forms of S1 and ACE2 in the presence of NB-DNJ, while varied outcomes resulted from dMM treatment. These results highlight the promising effects of NB-DNJ and its potential use as an off-label drug to treat SARS-CoV-2 infections.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

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.

Glycosylation shapes the efficacy and safety of diverse protein, gene and cell therapies

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.

Identification, characterization, and engineering of glycosylation in thrombolyticsa

Cardiovascular diseases, such as myocardial infarction, ischemic stroke, and pulmonary embolism, are the most common causes of disability and death worldwide. Blood clot hydrolysis by thrombolytic enzymes and thrombectomy are key clinical interventions. The most widely used thrombolytic enzyme is alteplase, which has been used in clinical practice since 1986. Another clinically used thrombolytic protein is tenecteplase, which has modified epitopes and engineered glycosylation sites, suggesting that carbohydrate modification in thrombolytic enzymes is a viable strategy for their improvement. This comprehensive review summarizes current knowledge on computational and experimental identification of glycosylation sites and glycan identity, together with methods used for their reengineering. Practical examples from previous studies focus on modification of glycosylations in thrombolytics, e.g., alteplase, tenecteplase, reteplase, urokinase, saruplase, and desmoteplase. Collected clinical data on these glycoproteins demonstrate the great potential of this engineering strategy. Outstanding combinatorics originating from multiple glycosylation sites and the vast variety of covalently attached glycan species can be addressed by directed evolution or rational design. Directed evolution pipelines would benefit from more efficient cell-free expression and high-throughput screening assays, while rational design must employ structure prediction by machine learning and in silico characterization by supercomputing. Perspectives on challenges and opportunities for improvement of thrombolytic enzymes by engineering and evolution of protein glycosylation are provided.

GlycoMME, a Markov modeling platform for studying N-glycosylation biosynthesis from glycomics data

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.

Preparing glycomics data for robust statistical analysis with GlyCompareCT

GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021).1.

CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells

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.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2017-2018

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization mass spectrometry (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2018. Also included are papers that describe methods appropriate to glycan and glycoprotein analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, new methods, matrices, derivatization, MALDI imaging, fragmentation and the use of arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Most of the applications are presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. The reported work shows increasing use of combined new techniques such as ion mobility and highlights the impact that MALDI imaging is having across a range of diciplines. MALDI is still an ideal technique for carbohydrate analysis and advancements in the technique and the range of applications continue steady progress.

In silico analysis of the human milk oligosaccharide glycome reveals key enzymes of their biosynthesis

Human milk oligosaccharides (HMOs) form the third most abundant component of human milk and are known to convey several benefits to the neonate, including protection from viral and bacterial pathogens, training of the immune system, and influencing the gut microbiome. As HMO production during lactation is driven by enzymes that are common to other glycosylation processes, we adapted a model of mucin-type GalNAc-linked glycosylation enzymes to act on free lactose. We identified a subset of 11 enzyme activities that can account for 206 of 226 distinct HMOs isolated from human milk and constructed a biosynthetic reaction network that identifies 5 new core HMO structures. A comparison of monosaccharide compositions demonstrated that the model was able to discriminate between two possible groups of intermediates between major subnetworks, and to assign possible structures to several previously uncharacterised HMOs. The effect of enzyme knockouts is presented, identifying β-1,4-galactosyltransferase and β-1,3-N-acetylglucosaminyltransferase as key enzyme activities involved in the generation of the observed HMO glycosylation patterns. The model also provides a synthesis chassis for the most common HMOs found in lactating mothers.

Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective

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.

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

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.

Hidden Relationships between N-Glycosylation and Disulfide Bonds in Individual Proteins

N-Glycosylation (NG) and disulfide bonds (DBs) are two prevalent co/post-translational modifications (PTMs) that are often conserved and coexist in membrane and secreted proteins involved in a large number of diseases. Both in the past and in recent times, the enzymes and chaperones regulating these PTMs have been constantly discovered to directly interact with each other or colocalize in the ER. However, beyond a few model proteins, how such cooperation affects N-glycan modification and disulfide bonding at selective sites in individual proteins is largely unknown. Here, we reviewed the literature to discover the current status in understanding the relationships between NG and DBs in individual proteins. Our results showed that more than 2700 human proteins carry both PTMs, and fewer than 2% of them have been investigated in the associations between NG and DBs. We summarized both these proteins with the reported relationships in the two PTMs and the tools used to discover the relationships. We hope that, by exposing this largely understudied field, more investigations can be encouraged to unveil the hidden relationships of NG and DBs in the majority of membranes and secreted proteins for pathophysiological understanding and biotherapeutic development.

Computational Modeling of O-Linked Glycan Biosynthesis in CHO Cells

Glycan biosynthesis simulation research has progressed remarkably since 1997, when the first mathematical model for N-glycan biosynthesis was proposed. An O-glycan model has also been developed to predict O-glycan biosynthesis pathways in both forward and reverse directions. In this work, we started with a set of O-glycan profiles of CHO cells transiently transfected with various combinations of glycosyltransferases. The aim was to develop a model that encapsulated all the enzymes in the CHO transfected cell lines. Due to computational power restrictions, we were forced to focus on a smaller set of glycan profiles, where we were able to propose an optimized set of kinetics parameters for each enzyme in the model. Using this optimized model we showed that the abundance of more processed glycans could be simulated compared to observed abundance, while predicting the abundance of glycans earlier in the pathway was less accurate. The data generated show that for the accurate prediction of O-linked glycosylation, additional factors need to be incorporated into the model to better reflect the experimental conditions.

An Interactive View of Glycosylation

The present chapter focuses on the interactive and explorative aspects of bioinformatics resources that have been recently released in glycobiology. The comparative analysis of data in a field where knowledge is scattered, incomplete, and disconnected from main biology requires efficient visualization, integration, and interactive tools that are currently only partially implemented. This overview highlights converging efforts toward building a consistent picture of protein glycosylation.

Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis

Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother’s fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data.

Glycomics can uncover important molecular changes but measured glycans are highly interconnected and incompatible with common statistical methods, introducing pitfalls during analysis. Here, the authors develop an approach to identify glycan dependencies across samples to facilitate comparative glycomics.

Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy

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.

N-Glycosylation Network Construction and Analysis to Modify Glycans on the Spike (S) Glycoprotein of SARS-CoV-2

Background: The N-glycan structure and composition of the spike (S) protein of SARS-CoV-2 are pertinent to vaccine development and efficacy.

Methods: We reconstructed the glycosylation network based on previously published mass spectrometry data using GNAT, a glycosylation network analysis tool. Our compilation of the network tool had 26 glycosyltransferase and glucosidase enzymes and could infer the pathway of glycosylation machinery based on glycans in the virus spike protein. Once the glycan biosynthesis pathway was generated, we simulated the effect of blocking specific enzymes—swainsonine or deoxynojirimycin for blocking mannosidase-II and indolizidine for blocking alpha-1,6-fucosyltransferase—to see how they would affect the biosynthesis network and the glycans that were synthesized.

Results: The N-glycan biosynthesis network of SARS-CoV-2 spike protein shows an elaborate enzymatic pathway with several intermediate glycans, along with the ones identified by mass spectrometric studies. Of the 26 enzymes, the following were involved—Man-Ia, MGAT1, MGAT2, MGAT4, MGAT5, B3GalT, B4GalT, Man-II, SiaT, ST3GalI, ST3GalVI, and FucT8. Blocking specific enzymes resulted in a substantially modified glycan profile of SARS-CoV-2.

Conclusion: Variations in the final N-glycan profile of the virus, given its site-specific microheterogeneity, are factors in the host response to the infection, vaccines, and antibodies. Heterogeneity in the N-glycan profile of the spike (S) protein and its potential effect on vaccine efficacy or adverse reactions to the vaccines remain unexplored. Here, we provide all the resources we generated—the glycans in the glycoCT xml format and the biosynthesis network for future work.

Towards rational glyco-engineering in CHO: from data to predictive models

Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.

Simulating the enzymes of ganglioside biosynthesis with Glycologue

Gangliosides are an important class of sialylated glycosphingolipids linked to ceramide that are a component of the mammalian cell surface, especially those of the central nervous system, where they function in intercellular recognition and communication. We describe an in silico method for determining the metabolic pathways leading to the most common gangliosides, based on the known enzymes of their biosynthesis. A network of 41 glycolipids is produced by the actions of the 10 enzymes included in the model. The different ganglioside nomenclature systems in common use are compared and a systematic variant of the widely used Svennerholm nomenclature is described. Knockouts of specific enzyme activities are used to simulate congenital defects in ganglioside biosynthesis, and altered ganglioside status in cancer, and the effects on network structure are predicted. The simulator is available at the Glycologue website, https://glycologue.org/.

Incorporating RNA-Seq transcriptomics into glycosylation-integrating metabolic network modelling kinetics: Multiomic Chinese hamster ovary (CHO) cell bioreactors

In this work, the kinetic model based on the previously developed metabolic and glycan reaction networks of the ovarian cells of the Chinese hamster ovary (CHO) cell line was improved by the inclusion of transcriptomic data that took into account the values of the RPKM gene (Reads per Kilobase of Exon per Million Reads Mapped). The transcriptomic (RNASeq) data were obtained together with metabolic and glycan data from the literature, and the concentrations with RPKM values were collected at several points in time from two fed-batch processes. First, the fluxes were determined by regression analysis of the metabolic data, then these fluxes were corrected by using the fold change in gene expression as a measure of enzyme concentrations. Next, the corrected fluxes in the kinetic model were used to calculate the concentration profiles of the metabolites, and literature data were used to evaluate the predicted results of the model. Compared to other studies where the concentration profiles of CHO cell metabolites were described using a kinetic model without consideration of RNA-Seq data to correct the fluxes, this model is unique. The additional integration of transcriptomic data led to better predictions of metabolic concentrations in the fed-batch process, which is a significant improvement of the modelling technique used.

A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)

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.

Challenges for biosimilars: focus on rheumatoid arthritis

Healthcare systems worldwide are struggling to find ways to fund the cost of innovative treatments such as gene therapies, regenerative medicine, and monoclonal antibodies (mAbs). As the world's best known mAbs are close to facing patent expirations, the biosimilars market is poised to grow with the hope of bringing prices down for cancer treatment and autoimmune disorders, however, this has yet to be realized. The development costs of biosimilars are significantly higher than their generic equivalents due to therapeutic equivalence trials and higher manufacturing costs. It is imperative that academics and relevant companies understand the costs and stages associated with biologics processing. This article brings these costs to the forefront with a focus on biosimilars being developed for Rheumatoid Arthritis (RA). mAbs have remarkably changed the treatment landscape, establishing their superior efficacy over traditional small chemicals. Five blockbuster TNFα mAbs, considered as first line biologics against RA, are either at the end of their patent life or have already expired and manufacturers are seeking to capture a significant portion of that market. Although in principle, market-share should be available, withstanding that the challenges regarding the compliance and regulations are being resolved, particularly with regards to variation in the glycosylation patterns and challenges associated with manufacturing. Glycan variants can significantly affect the quality attributes requiring characterization throughout production. Successful penetration of biologics can drive down prices and this will be a welcome change for patients and the healthcare providers. Herein we review the biologic TNFα inhibitors, which are on the market, in development, and the challenges being faced by biosimilar manufacturers.

Protein Glycoengineering: An Approach for Improving Protein Properties

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.

Modeling Glycan Processing Reveals Golgi-Enzyme Homeostasis upon Trafficking Defects and Cellular Differentiation

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.

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Developed a stochastic model of N-glycosylation coupled with Bayesian fitting

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Validated predicted changes of Golgi organization in trafficking mutants

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Model pinpointed functionally relevant glycan alterations in osteogenesis

Harnessing the potential of artificial neural networks for predicting protein glycosylation

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.

A Markov model of glycosylation elucidates isozyme specificity and glycosyltransferase interactions for glycoengineering

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.

Mechanistic reconstruction of glycoprotein secretion through monitoring of intracellular N-glycan processing

N-linked glycosylation plays a fundamental role in determining the thermodynamic stability of proteins and is involved in multiple key biological processes. The mechanistic understanding of the intracellular machinery responsible for the stepwise biosynthesis of N-glycans is still incomplete due to limited understanding of in vivo kinetics of N-glycan processing along the secretory pathway. We present a glycoproteomics approach to monitor the processing of site-specific N-glycans in CHO cells. On the basis of a model-based analysis of structure-specific turnover rates, we provide a kinetic description of intracellular N-glycan processing along the entire secretory pathway. This approach refines and further extends the current knowledge on N-glycans biosynthesis and provides a basis to quantify alterations in the glycoprotein processing machinery.

The N-Glycosylation Processing Potential of the Mammalian Golgi Apparatus

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.

Glycoengineering in CHO Cells: Advances in Systems Biology

For several decades, glycoprotein biologics have been successfully produced from Chinese hamster ovary (CHO) cells. The therapeutic efficacy and potency of glycoprotein biologics are often dictated by their post-translational modifications, particularly glycosylation, which unlike protein synthesis, is a non-templated process. Consequently, both native and recombinant glycoprotein production generate heterogeneous mixtures containing variable amounts of different glycoforms. Stability, potency, plasma half-life, and immunogenicity of the glycoprotein biologic are directly influenced by the glycoforms. Recently, CHO cells have also been explored for production of therapeutic glycosaminoglycans (e.g., heparin), which presents similar challenges as producing glycoproteins biologics. Approaches to controlling heterogeneity in CHO cells and directing the biosynthetic process toward desired glycoforms are not well understood. A systems biology approach combining different technologies is needed for complete understanding of the molecular processes accounting for this variability and to open up new venues in cell line development. In this review, we describe several advances in genetic manipulation, modeling, and glycan and glycoprotein analysis that together will provide new strategies for glycoengineering of CHO cells with desired or enhanced glycosylation capabilities.

The emerging role of systems biology for engineering protein production in CHO cells

To meet the ever-growing demand for effective, safe, and affordable protein therapeutics, decades of intense efforts have aimed to maximize the quantity and quality of recombinant proteins produced in CHO cells. Bioprocessing innovations and cell engineering efforts have improved product titer; however, uncharacterized cellular processes and gene regulatory mechanisms still hinder cell growth, specific productivity, and protein quality. Herein, we summarize recent advances in systems biology and data-driven approaches aiming to unravel how molecular pathways, cellular processes, and extrinsic factors (e.g. media supplementation) influence recombinant protein production. In particular, as the available omics data for CHO cells continue to grow, predictive models and screens will be increasingly used to unravel the biological drivers of protein production, which can be used with emerging genome editing technologies to rationally engineer cells to further control the quantity, quality and affordability of many biologic drugs.