A computational framework for the automated construction of glycosylation reaction networks

GlyCompute: towards the automated analysis of protein N-linked glycosylation kinetics via an open-source computational framework

Understanding the complex biosynthetic pathways of glycosylation is crucial for the expanding field of glycosciences. Computer-aided glycosylation analysis has greatly benefited in recent years from the development of tools found in web-based portals and open-source libraries. However, the in silico analysis of cellular glycosylation kinetics is underrepresented in current glycoscience-related tools and databases. This could be partly attributed to the limited accessibility of kinetic models developed using proprietary software and the difficulty in reliably parameterising such models. This work aims to address these challenges by proposing GlyCompute, an open-source framework demonstrating a novel, streamlined approach for the assembly, simulation, and parameterisation of kinetic models of protein N-linked glycosylation. Specifically, given one or more sets of experimentally observed N-glycan structures and their relative abundances, minimum representations of a glycosylation reaction network are generated. The topology of the resulting networks is then used to automatically assemble the material balances and kinetic mechanisms underpinning the mathematical model. To match the experimentally observed relative abundances, a sequential parameter estimation strategy using Bayesian inference is proposed, with stages determined automatically based on the underlying network topology. The proposed framework was tested on a case study involving the simultaneous fitting of the kinetic model to two protein N-linked glycoprofiles produced by the same CHO cell culture, showing good agreement with experimental observations. We envision that GlyCompute could help glycoscientists gain quantitative insights into the effect of enzyme kinetics and their perturbations on experimentally observed glycoprofiles in biomanufacturing and clinical settings.

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.

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.

GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology

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.

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.

O-Glycologue: A Formal-Language-Based Generator of O-Glycosylation Networks

The web application O-Glycologue provides an online simulation of the biosynthetic enzymes of O-linked glycosylation, using a knowledge-based system described previously. Glycans can be imported in GlycoCT condensed format, or else as IUPAC condensed names, and passed as substrates to the enzymes, which are modeled as regular-expression-based substitutions on strings. The resulting networks of reactions can be exported as SBML. The effects of knocking out different sets of enzyme activities can be compared. A method is provided for predicting the enzymes required to produce a given substrate, using an O-glycan from human gastric mucin as an example. The system has been adapted to other systems of glycosylation enzymes, and an application to ganglioside oligosaccharide synthesis is demonstrated. O-Glycologue is available at https://glycologue.org/o/ .

Functions of Glycosylation and Related Web Resources for Its Prediction

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.

Comparative Glycomics Analysis of Mass Spectrometry Data

Glycan profiling is a common strategy that is used to determine the distribution of N-linked glycans, O-linked glycans and glycolipid associated complex carbohydrate structures that are part of various cell and tissue sources. Such data are central to our understanding of functional glycomics, and this knowledge can also be used for pathway construction and other applications in the field of Systems Glycobiology. Glycans released from cell/tissue samples are often studied in their free-form. They can also be functionalized with aglycones like 2-aminobenzamide (2AB) and procainamide to enhance separation and improve ionization during liquid chromatography/mass spectrometry. Additionally, these released glycans may be permethylated in order to improve glycan quantitation. In such work, besides studying the glycans in a single sample, there is also interest in comparing multiple samples in order to determine underlying similarities and differences, for example in terms of specific epitopes that are changed when cells of the same origin differentiate along different pathways. The current chapter describes the development and usage of cGlyco ("comparative Glycomics"), an open-source program that can be used to compare data from multiple mass spectrometry runs. As an example, we apply cGlyco to compare the glycan profile of multiple MALDI-TOF glycomics profiling data collected by core-C of the Consortium for Functional Glycomics (CFG).

Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication

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.

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.

glypy: An Open Source Glycoinformatics Library

Glycoinformatics is a critical resource for the study of glycobiology, and glycobiology is a necessary component for understanding the complex interface between intra- and extracellular spaces. Despite this, there is limited software available to scientists studying these topics, requiring each to create fundamental data structures and representations anew for each of their applications. This leads to poor uptake of standardization and loss of focus on the real problems. We present glypy, a library written in Python for reading, writing, manipulating, and transforming glycans at several levels of precision. In addition to understanding several common formats for textual representation of glycans, the library also provides application programming interfaces (APIs) for major community databases, including GlyTouCan and UnicarbKB. The library is freely available under the Apache 2 common license with source code available at https://github.com/mobiusklein/ and documentation at https://glypy.readthedocs.io/ .

Harnessing glycoenzyme engineering for synthesis of bioactive oligosaccharides

Combined with chemical synthesis, the use of glycoenzyme biocatalysts has shown great synthetic potential over recent decades owing to their remarkable versatility in terms of substrates and regio- and stereoselectivity that allow structurally controlled synthesis of carbohydrates and glycoconjugates. Nonetheless, the lack of appropriate enzymatic tools with requisite properties in the natural diversity has hampered extensive exploration of enzyme-based synthetic routes to access relevant bioactive oligosaccharides, such as cell-surface glycans or prebiotics. With the remarkable progress in enzyme engineering, it has become possible to improve catalytic efficiency and physico-chemical properties of enzymes but also considerably extend the repertoire of accessible catalytic reactions and tailor novel substrate specificities. In this review, we intend to give a brief overview of the advantageous use of engineered glycoenzymes, sometimes in combination with chemical steps, for the synthesis of natural bioactive oligosaccharides or their precursors. The focus will be on examples resulting from the three main classes of glycoenzymes specialized in carbohydrate synthesis: glycosyltransferases, glycoside hydrolases and glycoside phosphorylases.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2013-2014

This review is the eighth 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 2014. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, and 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. Much of this material is 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. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 37:353-491, 2018.

DrawGlycan-SNFG: a robust tool to render glycans and glycopeptides with fragmentation information

Glycan or carbohydrate structures can be pictorially represented using symbolic nomenclatures. The symbol nomenclature for glycans (SNFG) contains 67 different monosaccharides represented using various colors and geometric shapes. A simple tool to convert International Union of Pure and Applied Chemistry (IUPAC) format text to SNFG will be useful for sketching glycans and glycopeptides. Such code can also enable the development of more sophisticated applications, where the visual representation of carbohydrate structures is necessary. To address this need, the current manuscript describes DrawGlycan-SNFG, a freely available, platform-independent, open-source tool. It allows: i. the display of glycans and glycopeptides from IUPAC-condensed text inputs and ii. the depiction of glycan and glycopeptide fragments. The online version of this program is provided with a user-friendly web interface at www.virtualglycome.org/DrawGlycan. Downloadable, stand-alone GUI (Graphical User Interface) version and the program source code are also available from this repository. DrawGlycan-SNFG will be useful for experimentalists looking for a ready to use, simple program for sketching carbohydrates and for software developers interested in incorporating SNFG into their program suite.

Glyco-Mapper: A Chinese hamster ovary (CHO) genome-specific glycosylation prediction tool

Glyco-Mapper is a novel systems biology product quality prediction tool created using a new framework termed: Discretized Reaction Network Modeling using Fuzzy Parameters (DReaM-zyP). Within Glyco-Mapper, users fix the nutrient feed composition and the glycosylation reaction fluxes to fit the model glycoform to the reference experimental glycoform, enabling cell-line specific glycoform predictions as a result of cell engineering strategies. Glyco-Mapper accurately predicts glycoforms associated with genetic alterations that result in the appearance or disappearance of one or more glycans with an accuracy, sensitivity, and specificity of 96%, 85%, and 97%, respectively, for publications between 1999 and 2014. The modeled glycoforms span a large range of glycoform engineering strategies, including the altered expression of glycosylation, nucleotide sugar transport, and metabolism genes, as well as an altered nutrient feeding strategy. A glycoprotein-producing CHO cell line reference glycoform was modeled and a novel Glyco-Mapper prediction was experimentally confirmed with an accuracy and specificity of 95% and 98%, respectively. Glyco-Mapper is a product quality prediction tool that provides a streamlined way to design host cell line genomes to achieve specific product quality attributes.

MALDI Mass Spectrometry Imaging of N-Linked Glycans in Tissues

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been used for two decades to profile the glycan constituents of biological samples. An adaptation of the method to tissues, MALDI mass spectrometry imaging (MALDI-MSI), allows high-throughput spatial profiling of hundreds to thousands of molecules within a single thin tissue section. The ability to profile N-glycans within tissues using MALDI-MSI is a recently developed method that allows identification and localization of 40 or more N-glycans. The key component is to apply a molecular coating of peptide-N-glycosidase to tissues, an enzyme that releases N-glycans from their protein carrier. In this chapter, the methods and approaches to robustly and reproducibly generate two-dimensional N-glycan tissue maps by MALDI-MSI workflows are summarized. Current strengths and limitations of the approach are discussed, as well as potential future applications of the method.

A Comprehensive, Open-source Platform for Mass Spectrometry-based Glycoproteomics Data Analysis

Glycosylation is among the most abundant and diverse protein post-translational modifications (PTMs) identified to date. The structural analysis of this PTM is challenging because of the diverse monosaccharides which are not conserved among organisms, the branched nature of glycans, their isomeric structures, and heterogeneity in the glycan distribution at a given site. Glycoproteomics experiments have adopted the traditional high-throughput LC-MSⁿ proteomics workflow to analyze site-specific glycosylation. However, comprehensive computational platforms for data analyses are scarce. To address this limitation, we present a comprehensive, open-source, modular software for glycoproteomics data analysis called GlycoPAT (GlycoProteomics Analysis Toolbox; freely available from www.VirtualGlycome.org/glycopat). The program includes three major advances: (1) "SmallGlyPep," a minimal linear representation of glycopeptides for MSⁿ data analysis. This format allows facile serial fragmentation of both the peptide backbone and PTM at one or more locations. (2) A novel scoring scheme based on calculation of the "Ensemble Score (ES)," a measure that scores and rank-orders MS/MS spectrum for N- and O-linked glycopeptides using cross-correlation and probability based analyses. (3) A false discovery rate (FDR) calculation scheme where decoy glycopeptides are created by simultaneously scrambling the amino acid sequence and by introducing artificial monosaccharides by perturbing the original sugar mass. Parallel computing facilities and user-friendly GUIs (Graphical User Interfaces) are also provided. GlycoPAT is used to catalogue site-specific glycosylation on simple glycoproteins, standard protein mixtures and human plasma cryoprecipitate samples in three common MS/MS fragmentation modes: CID, HCD and ETD. It is also used to identify 960 unique glycopeptides in cell lysates from prostate cancer cells. The results show that the simultaneous consideration of peptide and glycan fragmentation is necessary for high quality MSⁿ spectrum annotation in CID and HCD fragmentation modes. Additionally, they confirm the suitability of GlycoPAT to analyze shotgun glycoproteomics data.

What can mathematical modelling say about CHO metabolism and protein glycosylation?

Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation.

MALDI Mass Spectrometry Imaging of N-Linked Glycans in Cancer Tissues

Glycosylated proteins account for a majority of the posttranslation modifications of cell surface, secreted, and circulating proteins. Within the tumor microenvironment, the presence of immune cells, extracellular matrix proteins, cell surface receptors, and interactions between stroma and tumor cells are all processes mediated by glycan binding and recognition reactions. Changes in glycosylation during tumorigenesis are well documented to occur and affect all of these associated adhesion and regulatory functions. A MALDI imaging mass spectrometry (MALDI-IMS) workflow for profiling N-linked glycan distributions in fresh/frozen tissues and formalin-fixed paraffin-embedded tissues has recently been developed. The key to the approach is the application of a molecular coating of peptide-N-glycosidase to tissues, an enzyme that cleaves asparagine-linked glycans from their protein carrier. The released N-linked glycans can then be analyzed by MALDI-IMS directly on tissue. Generally 40 or more individual glycan structures are routinely detected, and when combined with histopathology localizations, tumor-specific glycans are readily grouped relative to nontumor regions and other structural features. This technique is a recent development and new approach in glycobiology and mass spectrometry imaging research methodology; thus, potential uses such as tumor-specific glycan biomarker panels and other applications are discussed.

Multi-level regulation of cellular glycosylation: from genes to transcript to enzyme to structure

Glycosylation is a ubiquitous mammalian post-translational modification that both decorates a majority of expressed proteins and regulates their function. Cellular glycan biosynthesis is facilitated by a few hundred enzymes that are collectively termed 'glycoenzymes'. The expression and activity of these enzymes is controlled at the transcription, translation and post-translation levels. New wet-lab advances are providing analytical methods to collect large-scale data at these multiple levels, relational databases are starting to collate these results, and computer models are beginning to integrate this information across scales in order to gain new knowledge. These activities are likely to enable the qualitative and quantitative mapping of pathways regulating glycan production and function in proteins, cells and tissue.

A systematic framework to derive N-glycan biosynthesis process and the automated construction of glycosylation networks

Background

Abnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental data for the identification of glycoforms contributing to disease. To overcome this limitation, the automatic construction of glycosylation reaction networks in silico is a critical step.

Results

In this paper, a framework K2014 is developed to automatically construct N-glycosylation networks in MATLAB with the involvement of the 27 most-known enzyme reaction rules of 22 enzymes, as an extension of previous model KB2005. A toolbox named Glycosylation Network Analysis Toolbox (GNAT) is applied to define network properties systematically, including linkages, stereochemical specificity and reaction conditions of enzymes. Our network shows a strong ability to predict a wider range of glycans produced by the enzymes encountered in the Golgi Apparatus in human cell expression systems.

Conclusions

Our results demonstrate a better understanding of the underlying glycosylation process and the potential of systems glycobiology tools for analyzing conventional biochemical or mass spectrometry-based experimental data quantitatively in a more realistic and practical way.

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.

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.

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.

ST3Gal-4 is the primary sialyltransferase regulating the synthesis of E-, P-, and L-selectin ligands on human myeloid leukocytes

The precise glycosyltransferase enzymes that mediate selectin-ligand biosynthesis in human leukocytes are unknown. This knowledge is important because selectin-mediated cell tethering and rolling is a critical component of both normal immune response and various vascular disorders. We evaluated the role of 3 α(2,3)sialyltransferases, ST3Gal-3, -4, and -6, which act on the type II N-Acetyllactosamine structure (Galβ1,4GlcNAc) to create sialyl Lewis-X (sLe(X)) and related sialofucosylated glycans on human leukocytes of myeloid lineage. These genes were either silenced using lentiviral short hairpin RNA (shRNA) or functionally ablated using the clustered regularly interspaced short palindromic repeat/Cas9 technology. The results show that ST3Gal-4, but not ST3Gal-3 or -6, is the major sialyltransferase regulating the biosynthesis of E-, P-, and L-selectin ligands in humans. Reduction in ST3Gal-4 activity lowered cell-surface HECA-452 epitope expression by 75% to 95%. Glycomics profiling of knockouts demonstrate an almost complete loss of the sLe(X) epitope on both leukocyte N- and O-glycans. In cell-adhesion studies, ST3Gal-4 knockdown/knockout cells displayed 90% to 100% reduction in tethering and rolling density on all selectins. ST3Gal-4 silencing in neutrophils derived from human CD34(+) hematopoietic stem cells also resulted in 80% to 90% reduction in cell adhesion to all selectins. Overall, a single sialyltransferase regulates selectin-ligand biosynthesis in human leukocytes, unlike mice where multiple enzymes contribute to this function.