Systems glycobiology for glycoengineering

Glycoengineered recombinant alpha1-antitrypsin results in comparable in vitro and in vivo activities to human plasma-derived protein

Alpha-1-antitrypsin (A1AT) is a multifunctional, clinically important, high value therapeutic glycoprotein that can be used for the treatment of many diseases such as alpha-1-antitrypsin deficiency, diabetes, graft-versus-host-disease, cystic fibrosis and various viral infections. Currently, the only FDA-approved treatment for A1AT disorders is intravenous augmentation therapy with human plasma-derived A1AT. In addition to its limited supply, this approach poses a risk of infection transmission, since it uses therapeutic A1AT harvested from donors. To address these issues, we sought to generate recombinant human A1AT (rhA1AT) that is chemically and biologically indistinguishable from its plasma-derived counterpart using glycoengineered Chinese Hamster Ovary (geCHO-L) cells. By deleting nine key genes that are part of the CHO glycosylation machinery and expressing the human ST6GAL1 and A1AT genes, we obtained stable, high producing geCHO-L lines that produced rhA1AT having an identical glycoprofile to plasma-derived A1AT (pdA1AT). Additionally, the rhA1AT demonstrated in vitro activity and in vivo half-life comparable to commercial pdA1AT. Thus, we anticipate that this platform will help produce human-like recombinant plasma proteins, thereby providing a more sustainable and reliable source of therapeutics that are cost-effective and better-controlled with regard to purity, clinical safety and quality.

Applying transcriptomics to studyglycosylation at the cell type level

The complex multi-step process of glycosylation occurs in a single cell, yet current analytics generally cannot measure the output (the glycome) of a single cell. Here, we addressed this discordance by investigating how single cell RNA-seq data can be used to characterize the state of the glycosylation machinery and metabolic network in a single cell. The metabolic network involves 214 glycosylation and modification enzymes outlined in our previously built atlas of cellular glycosylation pathways. We studied differential mRNA regulation of enzymes at the organ and single cell level, finding that most of the general protein and lipid oligosaccharide scaffolds are produced by enzymes exhibiting limited transcriptional regulation among cells. We predict key enzymes within different glycosylation pathways to be highly transcriptionally regulated as regulatable hotspots of the cellular glycome. We designed the Glycopacity software that enables investigators to extract and interpret glycosylation information from transcriptome data and define hotspots of regulation.

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RNA-seq can provide information on the glycosylation metabolic network state

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It is possible to readout glycosylation capacity from single cell RNA-seq data

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Genes regulating the biosynthesis of common glycan scaffolds show little regulation

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Key enzymes in the glycosylation network are predicted to be regulatable hotspots

Building better biobetters: From fundamentals to industrial application

Biological drugs or biopharmaceuticals off patent open a large market for biosimilars and biobetters, follow-on biologics. Biobetters, in particular, are new drugs designed from existing ones with improved properties such as higher selectivity, stability, half-life and/or lower toxicity/immunogenicity. Glycosylation is one of the most used strategies to improve biological drugs, nonetheless bioconjugation is an additional alternative and refers to the covalent attachment of polymers to biological drugs. Extensive research on novel polymers is underway, nonetheless PEGylation is still the best alternative with the longest clinical track record. Innovative trends based on genetic engineering techniques such as fusion proteins and PASylation are also promising. In this review, all these alternatives wereexplored as well as current market trends, legislation and future perspectives.

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.

Probabilistic model by Bayesian network for the prediction of antibody glycosylation in perfusion and fed-batch cell cultures

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.

Promiscuity and specificity of eukaryotic glycosyltransferases

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.

Control of IgG glycosylation in CHO cell perfusion cultures by GReBA mathematical model supported by a novel targeted feed, TAFE

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.

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.

Golgi compartments enable controlled biomolecular assembly using promiscuous enzymes

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.

Human T cell glycosylation and implications on immune therapy for cancer

Glycosylation is an important post-translational modification, giving rise to a diverse and abundant repertoire of glycans on the cell surface, collectively known as the glycome. When focusing on immunity, glycans are indispensable in virtually all signaling and cell-cell interactions. More specifically, glycans have been shown to regulate key pathophysiological steps within T cell biology such as T cell development, thymocyte selection, T cell activity and signaling as well as T cell differentiation and proliferation. They are of major importance in determining the interaction of human T cells with tumor cells. In this review, we will describe the role of glycosylation of human T cells in more depth, elaborate on the importance of glycosylation in the interaction of human T cells with tumor cells and discuss the potential of cancer immunotherapies that are based on manipulating the glycome functions at the tumor immune interface.^1,2

Glycosylated Biotherapeutics: Immunological Effects of N-Glycolylneuraminic Acid

The emerging field of biotherapeutics provides successful treatments for various diseases, yet immunogenicity and limited efficacy remain major concerns for many products. Glycosylation is a key factor determining the pharmacological properties of biotherapeutics, including their stability, solubility, bioavailability, pharmacokinetics, and immunogenicity. Hence, an increased attention is directed at optimizing the glycosylation properties of biotherapeutics. Currently, most biotherapeutics are produced in non-human mammalian cells in light of their ability to produce human-like glycosylation. However, most mammals produce the sialic acid N-glycolylneuraminic acid (Neu5Gc), while humans cannot due to a specific genetic defect. Humans consume Neu5Gc in their diet from mammalian derived foods (red meat and dairy) and produce polyclonal antibodies against diverse Neu5Gc-glycans. Moreover, Neu5Gc can metabolically incorporate into human cells and become presented on surface or secreted glycans, glycoproteins, and glycolipids. Several studies in mice suggested that the combination of Neu5Gc-containing epitopes and anti-Neu5Gc antibodies could contribute to exacerbation of chronic inflammation-mediated diseases (e.g., cancer, cardiovascular diseases, and autoimmunity). This could potentially become complicated with exposure to Neu5Gc-containing biotherapeutics, bio-devices or xenografts. Indeed, Neu5Gc can be found on various approved and marketed biotherapeutics. Here, we provide a perspective review on the possible consequences of Neu5Gc glycosylation of therapeutic protein drugs due to the limited published evidence of Neu5Gc glycosylation on marketed biotherapeutics and studies on their putative effects on immunogenicity, drug efficacy, and safety.

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

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.

Glycan Residues Balance Analysis - GReBA: A novel model for the N-linked glycosylation of IgG produced by CHO cells

The structure of N-linked glycosylation is a very important quality attribute for therapeutic monoclonal antibodies. Different carbon sources in cell culture media, such as mannose and galactose, have been reported to have different influences on the glycosylation patterns. Accurate prediction and control of the glycosylation profile are important for the process development of mammalian cell cultures. In this study, a mathematical model, that we named Glycan Residues Balance Analysis (GReBA), was developed based on the concept of Elementary Flux Mode (EFM), and used to predict the glycosylation profile for steady state cell cultures. Experiments were carried out in pseudo-perfusion cultivation of antibody producing Chinese Hamster Ovary (CHO) cells with various concentrations and combinations of glucose, mannose and galactose. Cultivation of CHO cells with mannose or the combinations of mannose and galactose resulted in decreased lactate and ammonium production, and more matured glycosylation patterns compared to the cultures with glucose. Furthermore, the growth rate and IgG productivity were similar in all the conditions. When the cells were cultured with galactose alone, lactate was fed as well to be used as complementary carbon source, leading to cell growth rate and IgG productivity comparable to feeding the other sugars. The data of the glycoprofiles were used for training the model, and then to simulate the glycosylation changes with varying the concentrations of mannose and galactose. In this study we showed that the GReBA model had a good predictive capacity of the N-linked glycosylation. The GReBA can be used as a guidance for development of glycoprotein cultivation processes.

Translation of genome to glycome: role of the Golgi apparatus

Glycans are one of the four biopolymers of the cell and they play important roles in cellular and organismal physiology. They consist of both linear and branched structures and are synthesized in a nontemplated manner in the secretory pathway of mammalian cells with the Golgi apparatus playing a key role in the process. In spite of the absence of a template, the glycans synthesized by a cell are not a random collection of possible glycan structures but a distribution of specific glycans in defined quantities that is unique to each cell type (Cell type here refers to distinct cell forms present in an organism that can be distinguished based on morphological, phenotypic and/or molecular criteria.) While information to produce cell type-specific glycans is encoded in the genome, how this information is translated into cell type-specific glycome (Glycome refers to the quantitative distribution of all glycan structures present in a given cell type.) is not completely understood. We summarize here the factors that are known to influence the fidelity of glycan biosynthesis and integrate them into known glycosylation pathways so as to rationalize the translation of genetic information to cell type-specific glycome.

Glycoengineering of HEK293 cells to produce high-mannose-type N-glycan structures

Therapeutic proteins are a developing part of the modern biopharmaceutical industry, providing novel therapies to intractable diseases including cancers and autoimmune diseases. The human embryonic kidney 293 (HEK293) cell line has been widely used to produce recombinant proteins in both basic science and industry. The heterogeneity of glycan structures is one of the most challenging issues in the production of therapeutic proteins. Previously, we knocked out genes encoding α1,2-mannosidase-Is, MAN1A1, MAN1A2 and MAN1B1, in HEK293 cells, establishing a triple-knockout (T-KO) cell line, which produced recombinant protein with mainly high-mannose-type N-glycans. Here, we further knocked out MAN1C1 and MGAT1 encoding another Golgi α1,2-mannosidase-I and N-acetylglucosaminyltransferase-I, respectively, based on the T-KO cells. Two recombinant proteins, lysosomal acid lipase (LIPA) and immunoglobulin G1 (IgG1), were expressed in the quadruple-KO (QD-KO) and quintuple-KO (QT-KO) cell lines. Glycan structural analysis revealed that all the hybrid-type and complex-type N-glycans were eliminated, and only the high-mannose-type N-glycans were detected among the recombinant proteins prepared from the QD-KO and QT-KO cells. Overexpression of the oncogenes MYC and MYCN recovered the slow growth in QD-KO and QT-KO without changing the glycan structures. Our results suggest that these cell lines could be suitable platforms to produce homogeneous therapeutic proteins.

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.

CHO-Omics Review: The Impact of Current and Emerging Technologies on Chinese Hamster Ovary Based Bioproduction

CHO cells are the most prevalent platform for modern bio-therapeutic production. Currently, there are several CHO cell lines used in bioproduction with distinct characteristics and unique genotypes and phenotypes. These differences limit advances in productivity and quality that can be achieved by the most common approaches to bioprocess optimization and cell line engineering. Incorporating omics-based approaches into current bioproduction processes will complement traditional methodologies to maximize gains from CHO engineering and bioprocess improvements. In order to highlight the utility of omics technologies in CHO bioproduction, the authors discuss current applications as well as limitations of genomics, transcriptomics, proteomics, metabolomics, lipidomics, fluxomics, glycomics, and multi-omics approaches and the potential they hold for the future of bioproduction. Multiple omics approaches are currently being used to improve CHO bioprocesses; however, the application of these technologies is still limited. As more CHO-omic datasets become available and integrated into systems models, the authors expect significant gains in product yield and quality. While individual omics technologies provide incremental improvements in bioproduction, the authors will likely see the most significant gains by applying multi-omics and systems biology approaches to individual CHO cell lines.

Production of homogeneous glycoprotein with multisite modifications by an engineered N-glycosyltransferase mutant

Naturally occurring N-glycoproteins exhibit glycoform heterogeneity with respect to N-glycan sequon occupancy (macroheterogeneity) and glycan structure (microheterogeneity). However, access to well-defined glycoproteins is always important for both basic research and therapeutic purposes. As a result, there has been a substantial effort to identify and understand the catalytic properties of N-glycosyltransferases, enzymes that install the first glycan on the protein chain. In this study we found that ApNGT, a newly discovered cytoplasmic N-glycosyltransferase from Actinobacillus pleuropneumoniae, has strict selectivity toward the residues around the Asn of N-glycosylation sequon by screening a small library of synthetic peptides. The inherent stringency was subsequently demonstrated to be closely associated with a critical residue (Gln-469) of ApNGT which we propose hinders the access of bulky residues surrounding the occupied Asn into the active site. Site-saturated mutagenesis revealed that the introduction of small hydrophobic residues at the site cannot only weaken the stringency of ApNGT but can also contribute to enormous improvement of glycosylation efficiency against both short peptides and proteins. We then employed the most efficient mutant (Q469A) other than the wild-type ApNGT to produce a homogeneous glycoprotein carrying multiple (up to 10) N-glycans, demonstrating that this construct is a promising biocatalyst for potentially addressing the issue of macroheterogeneity in glycoprotein preparation.

A semi-empirical glycosylation model of a camelid monoclonal antibody under hypothermia cell culture conditions

The impact of cell culture environment on the glycan distribution of a monoclonal antibody (mAb) has been investigated through a combination of experiments and modeling. A newly developed CHO DUXB cell line was cultivated at two levels of initial Glutamine (Gln) concentrations (0, 4 mM) and incubation temperatures of (33 and 37 °C) in batch operation mode. Hypothermia was applied either through the entire culture duration or only during the post-exponential phase. Beyond reducing cell growth and increasing productivity, hypothermia significantly altered the galactosylation index profiles as compared to control conditions. A novel semi-empirical dynamic model was proposed for elucidating the connections between the extracellular cell culture conditions to galactosylation index. The developed model is based on a simplified balance of nucleotides sugars and on the correlation between sugars' levels to the galactosylation index (GI). The model predictions were found to be in a good agreement with the experimental data. The proposed empirical model is expected to be useful for controlling the glycoprofiles by manipulating culture conditions.

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model

Biosimilar drugs must closely resemble the pharmacological attributes of innovator products to ensure safety and efficacy to obtain regulatory approval. Glycosylation is one critical quality attribute that must be matched, but it is inherently difficult to control due to the complexity of its biogenesis. This usually implies that costly and time-consuming experimentation is required for clone identification and optimization of biosimilar glycosylation. Here, a computational method that utilizes a Markov model of glycosylation to predict optimal glycoengineering strategies to obtain a specific glycosylation profile with desired properties is described. The approach uses a genetic algorithm to find the required quantities to perturb glycosylation reaction rates that lead to the best possible match with a given glycosylation profile. Furthermore, the approach can be used to identify cell lines and clones that will require minimal intervention while achieving a glycoprofile that is most similar to the desired profile. Thus, this approach can facilitate biosimilar design by providing computational glycoengineering guidelines that can be generated with a minimal time and cost.

Glycan Microarray Reveal the Sweet Side of Cancer Vaccines

Advances in genomics and bioinformatics facilitated identification of tumor-specific neoantigens as optimal targets for cancer immunotherapy. In this hot topic, most efforts focus on mutant peptide antigens, overlooking tumor-associated glycosylation changes. Given the latest progress in glycomics, in this issue of Cell Chemical Biology, Xia et al. (2016) use glyco-antigen microarrays to investigate immune responses to whole cancer vaccines and provide important insights into vaccine efficacy.

Expression of Active Fluorophore Proteins in the Milk of Transgenic Pigs Bypassing the Secretory Pathway

We describe the expression of recombinant fluorescent proteins in the milk of two lines of transgenic pigs generated by Sleeping Beauty transposon-mediated genetic engineering. The Sleeping Beauty transposon consisted of an ubiquitously active CAGGS promoter driving a fluorophore cDNA, encoding either Venus or mCherry. Importantly, the fluorophore cDNAs did not encode for a signal peptide for the secretory pathway, and in previous studies of the transgenic animals a cytoplasmic localization of the fluorophore proteins was found. Unexpectedly, milk samples from lactating sows contained high levels of bioactive Venus or mCherry fluorophores. A detailed analysis suggested that exfoliated cells of the mammary epithelium carried the recombinant proteins passively into the milk. This is the first description of reporter fluorophore expression in the milk of livestock, and the findings may contribute to the development of an alternative concept for the production of bioactive recombinant proteins in the udder.

Glycan characterization of biopharmaceuticals: Updates and perspectives

Therapeutic proteins are rapidly becoming the most promising class of pharmaceuticals on the market due to their successful treatment of a vast array of serious diseases, such as cancers and immune disorders. Therapeutic proteins are produced using recombinant DNA technology. More than 60% of therapeutic proteins are posttranslationally modified following biosynthesis by the addition of N- or O-linked glycans. Glycosylation is the most common posttranslational modifications of proteins. However, it is also the most demanding and complex posttranslational modification from the analytical point of view. Moreover, research has shown that glycosylation significantly impacts stability, half-life, mechanism of action and safety of a therapeutic protein. Considering the exponential growth of biotherapeutics, this present review of the literature (2009-2015) focuses on the characterization of protein glycosylation, which has witnessed an improvement in methodology. Furthermore, it discusses current issues in the fields of production and characterization of therapeutic proteins. This review also highlights the problem of non-standard requirements for the approval of biosimilars with regard to their glycosylation and discusses recent developments and perspectives for improved glycan characterization.

A Knowledge-Based System for Display and Prediction of O-Glycosylation Network Behaviour in Response to Enzyme Knockouts

O-linked glycosylation is an important post-translational modification of mucin-type protein, changes to which are important biomarkers of cancer. For this study of the enzymes of O-glycosylation, we developed a shorthand notation for representing GalNAc-linked oligosaccharides, a method for their graphical interpretation, and a pattern-matching algorithm that generates networks of enzyme-catalysed reactions. Software for generating glycans from the enzyme activities is presented, and is also available online. The degree distributions of the resulting enzyme-reaction networks were found to be Poisson in nature. Simple graph-theoretic measures were used to characterise the resulting reaction networks. From a study of in-silico single-enzyme knockouts of each of 25 enzymes known to be involved in mucin O-glycan biosynthesis, six of them, β-1,4-galactosyltransferase (β4Gal-T4), four glycosyltransferases and one sulfotransferase, play the dominant role in determining O-glycan heterogeneity. In the absence of β4Gal-T4, all Lewis X, sialyl-Lewis X, Lewis Y and Sda/Cad glycoforms were eliminated, in contrast to knockouts of the N-acetylglucosaminyltransferases, which did not affect the relative abundances of O-glycans expressing these epitopes. A set of 244 experimentally determined mucin-type O-glycans obtained from the literature was used to validate the method, which was able to predict up to 98% of the most common structures obtained from human and engineered CHO cell glycoforms.

Dynamics of immature mAb glycoform secretion during CHO cell culture: An integrated modelling framework

Ensuring consistent glycosylation-associated quality of therapeutic monoclonal antibodies (mAbs) has become a priority in pharmaceutical bioprocessing given that the distribution and composition of the carbohydrates (glycans) bound to these molecules determines their therapeutic efficacy and immunogenicity. However, the interaction between bioprocess conditions, cellular metabolism and the intracellular process of glycosylation remains to be fully understood. To gain further insight into these interactions, we present a novel integrated modelling platform that links dynamic variations in mAb glycosylation with cellular secretory capacity. Two alternative mechanistic representations of how mAb specific productivity (qp ) influences glycosylation are compared. In the first, mAb glycosylation is modulated by the linear velocity with which secretory cargo traverses the Golgi apparatus. In the second, glycosylation is influenced by variations in Golgi volume. Within our modelling framework, both mechanisms accurately reproduce experimentally-observed dynamic changes in mAb glycosylation. In addition, an optimisation-based strategy has been developed to estimate the concentration of glycosylation enzymes required to minimise mAb glycoform variability. Our results suggest that the availability of glycosylation machinery relative to cellular secretory capacity may play a crucial role in mAb glycosylation. In the future, the modelling framework presented here may aid in selecting and engineering cell lines that ensure consistent mAb glycosylatio.

A Markov chain model for N-linked protein glycosylation--towards a low-parameter tool for model-driven glycoengineering

Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.

A novel probe based on phenylboronic acid functionalized carbon nanotubes for ultrasensitive carbohydrate determination in biofluids and semi-solid biotissues

An ultrasensitive SPME probe based on phenylboronic acid functionalized CNTs is applied for direct in vitro or in vivo recognition of carbohydrates in biofluids as well as semi-solid biotissues.

Carbohydrates are known to be involved in a wide range of biological and pathological processes. However, due to the presence of multiple hydroxyl groups, carbohydrate recognition is a particular challenge. Herein, we reported an ultrasensitive solid-phase microextraction (SPME) probe based on phenylboronic acid (PBA) functionalized carbon nanotubes (CNTs) for direct in vitro or in vivo recognition of carbohydrates in biofluids as well as semi-solid biotissues. The coating of the proposed probe possessed a 3D interconnected porous architecture formed by the stacking of CNTs. As a result, the binding capacity toward carbohydrates was excellent. The proposed approach was demonstrated to be much superior to most carbohydrate sensors, including higher sensitivity, wider linear range, and excellent qualitative ability in multi-carbohydrate systems. Thus, this approach opens up new avenues for the facile and efficient recognition of carbohydrates for important applications such as glycomics.

Carbohydrate structure database merged from bacterial, archaeal, plant and fungal parts

The Carbohydrate Structure Databases (CSDBs, http://csdb.glycoscience.ru) store structural, bibliographic, taxonomic, NMR spectroscopic, and other data on natural carbohydrates and their derivatives published in the scientific literature. The CSDB project was launched in 2005 for bacterial saccharides (as BCSDB). Currently, it includes two parts, the Bacterial CSDB and the Plant&Fungal CSDB. In March 2015, these databases were merged to the single CSDB. The combined CSDB includes information on bacterial and archaeal glycans and derivatives (the coverage is close to complete), as well as on plant and fungal glycans and glycoconjugates (almost all structures published up to 1998). CSDB is regularly updated via manual expert annotation of original publications. Both newly annotated data and data imported from other databases are manually curated. The CSDB data are exportable in a number of modern formats, such as GlycoRDF. CSDB provides additional services for simulation of (1)H, (13)C and 2D NMR spectra of saccharides, NMR-based structure prediction, glycan-based taxon clustering and other.

Using glyco-engineering to produce therapeutic proteins

INTRODUCTION

Glycans are increasingly important in the development of new biopharmaceuticals with optimized efficacy, half-life, and antigenicity. Current expression platforms for recombinant glycoprotein therapeutics typically do not produce homogeneous glycans and frequently display non-human glycans which may cause unwanted side effects. To circumvent these issues, glyco-engineering has been applied to different expression systems including mammalian cells, insect cells, yeast, and plants.

AREAS COVERED

This review summarizes recent developments in glyco-engineering focusing mainly on in vivo expression systems for recombinant proteins. The highlighted strategies aim at producing glycoproteins with homogeneous N- and O-linked glycans of defined composition.

EXPERT OPINION

Glyco-engineering of expression platforms is increasingly recognized as an important strategy to improve biopharmaceuticals. A better understanding and control of the factors leading to glycan heterogeneity will allow simplified production of recombinant glycoprotein therapeutics with less variation in terms of glycosylation. Further technological advances will have a major impact on manufacturing processes and may provide a completely new class of glycoprotein therapeutics with customized functions.

Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling

Eukaryotic cell lines, including Chinese hamster ovary cells, yeast, and insect cells, are invaluable hosts for the production of many recombinant proteins. With the advent of genomic resources, one can now leverage genome-scale computational modeling of cellular pathways to rationally engineer eukaryotic host cells. Genome-scale models of metabolism include all known biochemical reactions occurring in a specific cell. By describing these mathematically and using tools such as flux balance analysis, the models can simulate cell physiology and provide targets for cell engineering that could lead to enhanced cell viability, titer, and productivity. Here we review examples in which metabolic models in eukaryotic cell cultures have been used to rationally select targets for genetic modification, improve cellular metabolic capabilities, design media supplementation, and interpret high-throughput omics data. As more comprehensive models of metabolism and other cellular processes are developed for eukaryotic cell culture, these will enable further exciting developments in cell line engineering, thus accelerating recombinant protein production and biotechnology in the years to come.

CRISPR/Cas9-mediated genome engineering of CHO cell factories: Application and perspectives

Chinese hamster ovary (CHO) cells are the most widely used production host for therapeutic proteins. With the recent emergence of CHO genome sequences, CHO cell line engineering has taken on a new aspect through targeted genome editing. The bacterial clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system enables rapid, easy and efficient engineering of mammalian genomes. It has a wide range of applications from modification of individual genes to genome-wide screening or regulation of genes. Facile genome editing using CRISPR/Cas9 empowers researchers in the CHO community to elucidate the mechanistic basis behind high level production of proteins and product quality attributes of interest. In this review, we describe the basis of CRISPR/Cas9-mediated genome editing and its application for development of next generation CHO cell factories while highlighting both future perspectives and challenges. As one of the main drivers for the CHO systems biology era, genome engineering with CRISPR/Cas9 will pave the way for rational design of CHO cell factories.

Cell line development for biomanufacturing processes: recent advances and an outlook

At the core of a biomanufacturing process for recombinant proteins is the production cell line. It influences the productivity and product quality. Its characteristics also dictate process development, as the process is optimized to complement the producing cell to achieve the target productivity and quality. Advances in the past decade, from vector design to cell line screening, have greatly expanded our capability to attain producing cell lines with certain desired traits. Increasing availability of genomic and transcriptomic resources for industrially important cell lines coupled with advances in genome editing technology have opened new avenues for cell line development. These developments are poised to help biosimilar manufacturing, which requires targeting pre-defined product quality attributes, e.g., glycoform, to match the innovator's range. This review summarizes recent advances and discusses future possibilities in this area.

Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data

The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. 'Systems Glycobiology' is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or 'systems-level' view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell-surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post-translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation-related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize 'Big Data' stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole-cell level has been standardized, there is a need to integrate mass spectrometry derived site-specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between 'data' generated in wet-laboratory studies with 'knowledge' that can enhance our understanding of the glycome.

Engineering the supply chain for protein production/secretion in yeasts and mammalian cells

Metabolic bottlenecks play an increasing role in yeasts and mammalian cells applied for high-performance production of proteins, particularly of pharmaceutical ones that require complex posttranslational modifications. We review the present status and developments focusing on the rational metabolic engineering of such cells to optimize the supply chain for building blocks and energy. Methods comprise selection of beneficial genetic modifications, rational design of media and feeding strategies. Design of better producer cells based on whole genome-wide metabolic network analysis becomes increasingly possible. High-resolution methods of metabolic flux analysis for the complex networks in these compartmented cells are increasingly available. We discuss phenomena that are common to both types of organisms but also those that are different with respect to the supply chain for the production and secretion of pharmaceutical proteins.

Recent advances in engineering yeast for pharmaceutical protein production

Currently available systems and synthetic biology tools can be applied to yeast engineering for improved biopharmaceutical protein production.