Open access peer-reviewed chapter

Systems Glycobiology: Past, Present, and Future

Written By

Songül Yaşar Yıldız

Submitted: 01 November 2019 Reviewed: 25 March 2020 Published: 02 May 2020

DOI: 10.5772/intechopen.92267

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Glycobiology is a glycan-based field of study that focuses on the structure, function, and biology of carbohydrates, and glycomics is a sub-study of the field of glycobiology that aims to define structure/function of glycans in living organisms. With the popularity of the glycobiology and glycomics, application of computational modeling expanded in the scientific area of glycobiology over the last decades. The recent availability of progressive Wet-Lab methods in the field of glycobiology and glycomics is promising for the impact of systems biology on the research area of the glycome, an emerging field that is termed “systems glycobiology.” This chapter will summarize the up-to-date leading edge in the use of bioinformatics tools in the field of glycobiology. The chapter provides basic knowledge both for glycobiologists interested in the application of bioinformatics tools and scientists of computational biology interested in studying the glycome.


  • glycan
  • glycobiology
  • glycome
  • systems biology
  • systems glycobiology

1. Introduction

Glycans are long chains of carbohydrate-based polymers composed of repeating units of monosaccharide monomers bound together by glycosidic linkages. Complex and diverse glycans appear to be ever-present macromolecules in all cells in nature, and essential to all biological systems. Glycans play physical, structural, and metabolic roles in living organisms [1]. In the last century, knowledge on the biochemistry and biology of nucleic acids and proteins rapidly increased. Nevertheless, it has been much more difficult to understand the biology of glycans, which are main component of the cell surface [2]. The biosynthesis mechanism of glycans is totally different from those of nucleic acids and proteins. Biological mechanism of glycans is complex, which makes analysis of them extremely difficult and limits our understanding of mechanisms responsible for biological functions of glycans [3]. After the genomics revolution and development of high-throughput technologies, scientific interests increased to understand the characterization, function, and interaction of other significant biomolecules (e.g., DNA transcripts, proteins, lipids, and glycans) for the cell. These interests resulted in emergence of other omic types such as transcriptomics, proteomics, metabolomics, lipidomics and glycomics [4]. From the perspective of evolutionary conservation, conservation decreased in the order genomics, transcriptomics, proteomics, metabolomics, lipidomics, and glycomics. On the other hand, reverse order is present for informational diversity of these fields of omics (Figure 1) [5].

Figure 1.

The degree of evolutionary conservation and informational diversity for the omics fields.

With the progress in high-throughput technologies, studies on glycobiology increased to screen cells quickly and generate huge glycomics data sets. Moreover, advanced analytical techniques and tools for data analysis provide possibility to improve high-throughput techniques for screening glycans as a marker of diseases and to classify structure of glycans in therapeutic proteins [6].


2. Glycans

Glycans are linear or branched sugar macromolecules composed of repeating monosaccharides linked glycosidically. Beside nucleic acids and protein, glycans are known as the third dimension in molecular biology [7, 8]. These macromolecules can be found in the form of heteropolysaccharides or homopolysaccharides. Furthermore, glycoconjugates (glycolipid, glycoprotein and proteoglycan), can be also considered as glycan despite the fact that the carbohydrate part of glycoconjugates are only oligosaccharides [9]. In glycoproteins, oligosaccharides and proteins can be linked in different forms, namely N-linked glycans and O-linked glycans. N-acetylglucosamine is linked to the amide side chain of asparagine in N-linked glycans. C-1 of N-acetylgalactosamine is linked to the hydroxyl function of serine or threonine in O-linked glycans [10].

With the increasing researches in glycoscience, many different roles of glycans in biological systems have been revealed in the last decades. Significant functions of glycans have been determined in numerous research areas such as immunity, development and differentiation, biopharmaceuticals, cancer, fertilization, blood types, infectious diseases, etc. Glycans are called as “cloths of cells” since they are present on the surface of the cell and responsible for the signaling and communications between cells. Glycans can be classified in several ways. Varki divided the biological roles of glycans into four main categories: (1) structural and modulatory roles, (2) extrinsic (interspecies) recognition of glycans, (3) intrinsic (intraspecies) recognition of glycans, and (4) molecular mimicry of host glycans. A total of 50 distinct roles are defined under these main categories [1].

Glycans perform huge range of biological function due to the diversity of them, and they have significant roles in several physiological and pathological events, such as cell growth, cell signaling, cell-cell interactions, differentiation, and tumor growth [11, 12, 13]. In biological systems, information is carried by glycans, which are significant biomarker candidates for many diseases such as cardiovascular diseases, deficiencies of immune system, genetically inherited disorders, several cancer types, and neurodegenerative diseases [14, 15, 16]. Alteration of glycan expression is observed during the development and progression of these diseases, which is caused by misregulated enzymes such as glycosyltransferases and glycosidases. As a result, altered glycan structures have potential use for the identification of these diseases at an early stage. Besides significant role of glycans in diagnosis and management of disease, they can be used as therapeutics, markers for identification and isolation of special cell types, and targets in discovery of drugs [17, 18, 19]. Moreover, glycans can be considered as an ideal target for vaccines due to the presence of them on the surface of several different pathogens and malignant cells. High affinity and exquisite specificity of other molecules to recognize glycans are a vital point of developments in the research of glycans and related diagnostics and therapeutic applications.


3. Glycomics

Glycosylation plays significant roles in many biological processes including growth and development of cell, tumor growth and metastasis, immune recognition and response, intercommunication of cells, and microbial pathogenesis. As a result, glycosylation of proteins is the one of the most common and significant posttranslational modifications of proteins [20, 21]. Furthermore, more than half of proteins undergo glycosylation [6]. Many issues such as genetic factors, nucleotide levels of monosaccharides, cytokines, metabolites, hormones, and ecological factors can affect and change glycosylation process [20, 21, 22, 23, 24]. Thus, integration of omics approaches (e.g., proteomics, genomics, transcriptomics, and metabolomics) to the field of glycobiology is essential to view the big picture of the whole biological system [20, 21, 25]. Furthermore, for the analysis of glycans and glycosylation pathways, many glycoinformatics tools and databases are now accessible [6].

Glycomics is one of the most recent types of omics area which is responsible for the structure and function evaluations of glycans in bio-systems [26]. Integrating glycomics to other fields of omics provides new system-scale insights in integrative biology [27].

Moreover, glycomics informs other crucial scholarships such as systems glycobiology and personalized glycomedicine that collectively aim to explain the role of glycans in person-to-person and between population variations in disease susceptibility and response to health interventions such as drugs, nutrition, and vaccines. Glycosylation is present in both normal and diseased individuals [1]. Abnormal glycosylation is observed in a variety of diseases. Difference between glycosylation patterns of healthy and diseased individuals can be used as glycobiomarkers in personalized medicine [28]. As a result, many new medical implications will be enabled by glycobiology and glycopathology [29]. Development of glycomedicine can be contributed by holistic approach of functional and structural glycomics, which have applications in therapy development, fine-tuning immunological responses and the performance of therapeutic antibodies and boosting immune responses [28, 30]. Many applications of glycan arrays are present in many fields, from basic biochemical research to biomedical applications [31]. In addition to shotgun glycan microarrays [32], cell-based array resource has been developed [33]. These developments enable deeper understanding of the many biological roles of the glycome. Nevertheless, multiplatform and multiomics technologies are expected to further extend the knowledge of molecular mechanisms of glycans.

3.1 Major glycomics techniques

Monosaccharides represent four free hydroxyl groups for the linkage of another monosaccharide. As a result of this, glycans have more complex structure compared to structure of peptides and nucleic acids. It is known that glycans are more than the sequential monosaccharides; monomer types, modifications, the position of modifications around the ring of sugar, glycopolymer branching, and linkages chirality are the factors that are responsible for the complexity. As a result, sequencing techniques used for peptides or DNA (Sanger or Edman sequencing) are not appropriate for glycans. Moreover, most of the glycans are present as a part of a glycoconjugate. Therefore, glycan part should be released from lipid or protein part, by the use of enzymatic or chemical methods and isolated for analysis.

In the last decades, a number of techniques developed and applied to determine structure of the glycans with different degrees of detail [34]. A traditional method is to label the glycoconjugates radioactively and then apply anionic exchange, gel filtration, or paper chromatographic analyses prior and subsequent to enzymatic or chemical treatments. Still, it is difficult to figure out the definition of the actual structure; in consequence, in earlier studies, if adequate amounts were present, gas chromatography together with mass spectrometry (GC-MS) and/or nuclear magnetic resonance (NMR) studies were performed. However, these analyses involve special expertise to perform the research and interpret the results, particularly if standards were unavailable to compare with results.

HPLC and UPLC have superseded simple chromatography systems in recent years, and radioactive labeling has been replaced by fluorescent labeling. Nowadays, variable columns such as graphitized carbon, reversed-phase (RP), anion exchange, normal phase, or hydrophilic interaction resins can be used along with suitable enzymatic/chemical treatments. A less used alternative is to analyze glycans at elevated pH. As a result of this, the hydroxyl side chain deprotonation occurs, that enables the usage of anion exchange together with amperometric detection (HPAEC-PAD). On the other hand, glycan structure cannot be defined only by HPLC retention times, and for the unknown structure, analyses in the absence of standards should be interpreted with attention [35].

With the improvements in the types and the sensitivity, contribution of mass spectrometer to studies of glycans and glycoconjugates has increased in the last decades [36, 37]. At first, for the analysis of variable types of glycopolymers from different sources, researchers used fast atom bombardment mass spectrometers (FAB-MS). For the analyses with FAB-MS, chemical modifications such as methylation and acetylation were required. As an alternative method, matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was developed and analysis of both permethylated and native glycans can be performed with MALDI-TOF MS. Furthermore, current numerous electrospray techniques with many detector types have significance in glycomics. Mainly, a significant point in MS-based analysis is the capability to obtain glycan fragments. Besides, the preparation and separation techniques are of great importance to obtain the best results. As a consequence, liquid chromatography-mass spectrometry (LC-MS) in a number of forms is in general necessary since glycans with low abundancy or poor ionization capacity can be suppressed in the case of whole glycome examination. Moreover, reanalysis after the treatment of a chemical and an enzyme results in maximization of the ability to obtain clear results from the existing data.

Glycan is generally a part of the glycoconjugate; thus, glycoproteomics and glycolipidomics that consider both peptide/lipid and glycan parts are significant fields. At this point, mass spectrometry technique comes into prominence [38]. Both glycan and polypeptide/lipid parts can be studied with this technique. On the other hand, glycan parts of glycoproteins and glycolipids can be in various forms even if the polypeptide/lipid part is same, defined as microheterogeneity. The nature of glycan modifications is non-template driven and that leads to mentioned microheterogeneity [39].

Blotting technique can be used for simple screens. Reagents such as lectins and anti-carbohydrate antibodies with low specificity are often used for this technique; as a result, misleading results are often obtained [40]. Still, lectins, and antiserums have significance for immune responses in animals. New array-based systems can provide essential clues on proteins bounded to glycans [41].


4. Systems glycobiology and integration of omics data sets

Developments in integrative informatics and systems biology of glycans based on a holistic approach can make available a more comprehensive analysis. It elucidates annotation of glycans, enzyme levels, abundances of glycans biosynthesis pathways, and other omics data sets which are complementary. Though, several tools are developed for proteomics and genomics data sets and standard bioinformatics approaches are used in these tools, the complex relationships between diverse components (such as glycans, enzymes, transporters, and sugar nucleotides) of the glycosylation process are not considered by most of the existing bioinformatics tools. Consequently, the use of these tools for glycomics data sets has some limitations. The genome does not encode glycans directly and unlike proteins, interconnected action of many enzymes provides assembly of glycans. Due to mentioned limitations, developments in glycan analysis tools and methods have been delayed and most of the present glycoinformatics tools are special for single type data analysis [42, 43, 44, 45]. For instance, database matching between obtained MS results and specific glycans in a glycan library is used as a mutual method for MS-based glycoprofiling for the purpose of individual peak annotation [46, 47]. If the complexity of glycosylation is wanted to be considered, enzymes of the organism which synthesize the studied glycans should generate glycan structures used for the annotation of the spectrum [48]. Due to this alignment, activities of enzyme and those structures assigned to each peak in the same spectrum will be consistent.

Although many omics approaches have significant progress in the last decades, existing techniques of bioinformatics are still unsatisfactory for the integration of varied data sets [49, 50, 51]. For instance, relations between expression levels of gene and specific glycan linkages abundance are investigated by statistical database-driven approaches, and these approaches could not predict quantity of detailed glycan distributions [50, 51]. This indicates the necessity of glycoinformatics and systems biology tools integration for the identification of glycan structure and these should be also linked to the information of gene expression responsible for glycosylation enzymes which synthesize these glycans. In order to understand levels of mRNA which is related with the distribution and quantity of glycans present within healthy and diseased cells, mathematical modeling of glycosylation is considered as a promising method [48, 49, 52].

Variability in the platform of analytical high-throughput experiments can be reduced by data integration approach. Increased confidence of biomarker predictions and recommendations can be obtained if different data from experiments such as glycogenes expression information or mass spectra profile confirm the results from integrative glycoinformatics and systems glycobiology tools. Although integrated glycoinformatics tools have limitations in analytical sensitivities, analysis and comparison of various results with various platforms are enabled by these tools [6].

The integration of glycomics with other various omics data is promising for further innovation in diagnosis and treatment of diseases [30]. The start point of multiomics data integration is to sort the data based on the omics level. In the following part, association between glycomics and other omics levels will be represented.

4.1 Genomics

Integration of glycomics with genetic sequence can be occur in a number of ways. For instance, glycosylation site can be gained or lost with the variation of sequence. A single-nucleotide polymorphism (SNP) affects glycosylation of prostate-specific antigen (PSA) and an altered function of it increases the risk of prostate cancer. Functional analysis indicated that the stability and structural conformation of PSA are affected by missense variant rs61752561, which causes an additional extra glycosylation site [53]. Furthermore, computational studies revealed that variations in cancer somatic cells have potential to cause gain or loss of glycosylation. In addition to SNP, variations in structure and abnormalities in cytogenetics could be integrated with glycomics. Cytogenetic abnormalities have been associated with glycome expression [54]. A particular glycosyltransferase can glycosylate numerous proteins, so genetic variants of it have extraordinary significance because function of many glycoproteins can be affected by a single difference in activity of enzyme. Several downstream pathways and cell metabolism can be affected by a genetic or epigenetic variant that is called pleiotropic effect of genetic or epigenetic variant on glycosylation [55].

4.2 Transcriptomics

Most of the glycomics research have been done at the level of transcriptome, which can be performed either at a particular locus or with a technology of microarray. In colorectal cancer (CRC), glycosyltransferase ST6GAL1 is associated with cancer, and altered ST6GAL1 expression was found by The Cancer Genome Atlas (TCGA) mining [56]. Moreover, in order to identify differential expression of glycosylation-related genes Saravanan et al. [57] used GLYCOv2 glycogene microarray technology. In the further studies, myeloma was compared with normal plasma cell samples and 60 upregulated and 20 downregulated genes were found among 243 genes in glycan-biosynthesis pathway [54]. A novel molecular signature that is enriched for enzymes of glycosylation was revealed by meta-analysis performed for gene expression of prostate cancer [58]. Additionally, hepatocellular carcinoma was investigated by reviewing gene expressions that are related with core fucosylation of the disease [59]. More systematic reviews and meta-analyses are required to develop reliable biomarkers.

4.3 Glycoproteomics

Studies on glycoproteomics include peptide structures, glycan structures, and sites of glycosylation [30]. Single site on the peptide chain can be glycosylated by different glycans, and by this way, glycans can modulate function of the protein [60]. In the literature, diverse techniques were associated with different phenotypes, for instance, breast cancer, colon cancer, liver cancer, skin cancer, ovary cancer, bladder cancer, and neurodegenerative diseases, and additionally, a number of structural variations including sialylation, fucosylation, degree of branching, and specific glycosyltransferases expression [61, 62, 63]. For instance, cerebrospinal fluid N-glycoproteomics is of significant importance in early diagnosis of Alzheimer’s disease. Glycosylation patterns were assessed in patients and therapeutics targets such as glycoenzymes were suggested [63]. For the diagnosis of pancreatic cancer, specific glycoforms together with protein levels should be measured to improve potential for diagnosis [64]. Glycoproteins constitute the majority of protein tumor markers approved by Food and Drug Administration (FDA), and they are also used currently in clinical practice. Many of these glycoproteins have alterations of glycosylation in cancer [60]. MUC-1 (CA15-3/CA27.29) [65] and plasminogen activator inhibitor (PAI-1) [66] are biomarkers of breast cancer; beta-human chorionic gonadotropin (Beta-hCG) [67] is biomarker of colorectal cancer; alpha-fetoprotein (AFP) [68] is a biomarker of liver cancer and germ cell tumors; chromogranin A (CgA) [69] is a biomarker of neuroendocrine tumors; MUC16 (CA-125) [70] and HE4 [71] are biomarkers of ovarian cancer; and many other biomarkers are present for a variety type of cancer. Most of the results in the existing publications are heterogeneous; thus, systematic integrative reviews of the literature are required for further development of glycoproteomics.

4.4 Metabolomics

Metabolomics is the large-scale study of the small molecule substrates that investigates variations in the metabolites within cells, biofluids, tissues, or organism. Metabolomics and glycomics were investigated in the research of post-traumatic stress [72]. According to the researchers of this study, these biomarkers together with omics markers should be integrated to understand the biological differences responsible for this stress. For discovery of liver cancer biomarker, proteomics, glycomics, and metabolomics were integrated and this integration enhanced performance when compared to separate omics data [73]. Physiological and pathological conditions are reflected by metabolomic and glycomic data in individuals. Similar to metabolites, small glycans can be quantified easily [74]. Human Metabolome Database (HMDB) is the most inclusive metabolite source that offers significant resource for the discovery of biomarkers in glycomics [75].

4.5 Glycolipidomics

Glycolipidomics is a scientific field that identifies and quantifies glycolipids. For the determination of physiological and pathological conditions of individual, glycolipids can be used as a specific biomarker. They take role in development of neurological and neurodegenerative diseases, such as Lewy body dementia, Alzheimer’s disease, Parkinson’s disease, and frontotemporal dementia [30]. Furthermore, glycosphingolipids are associated with cancer and they are promising molecules for diagnosis as biomarkers and for malignant tumor immunotherapy as target [76]. More recently, Dehelean et al. [77] reviewed trends in the discovery of glycolipid biomarker by MS.

4.6 Interactomics

Interactomics is the research field that investigates whole set of interactions between molecules including glycans. Interaction of glycans with glycan-binding proteins (GBPs) is of significant importance in immune response, signaling, cell recognition, infections, neurodegenerative diseases, and cancer. High-throughput technologies ease studies also on interactomics [78]. UniLectin3D is a database that catalog lectins that are most studied GBPs. Database consists of curated information on 3D structures and interacting ligands [79]. Lectin-glycan interaction on surface of the cell is a significant factor for the regulation in corneal biology (i.e., corneal infection) and pathophysiology (i.e., inflammation) [80]. The whole protein-glycan interactome information has not been obtained yet [41]. For future studies, estimated number of interactions is of importance. GenProBiS is a bioinformatics tool that analyzes binding sites between peptide-peptide, peptide-nucleic acid, and peptide-compound and also sites of glycosylation and other posttranslational modifications. Furthermore, it provides maps between sequence variations and structure of protein. More developments of bioinformatics tools analyzing huge data will prioritize the objections for experimental verification and provide contribution to interactomics development.

4.7 Other omics fields

In future studies, many other omics fields should be associated with glycomics such as comparative genomics, epigenomics, regulomics, NcRNomics, MiRNomics, LncRNomics, etc. Although glycomics is the significant field related with molecular interactions, information about how these complex processes controlled by regulatory network is still inadequate. In addition to classic omics fields, omics applications such as iatromics, environmental omics, pharmacogenomics, and nutrigenomics should also be reviewed.


5. Bioinformatics tools and databases

Glycoinformatics combines bioinformatics tools with glycome. Glycomics data is collected by the tools and databases to investigate, reveal, and associate with other repository of related data of proteomics, genomics, and interactomics. Commonly used tools and databases are summarized in Table 1.

Name Description Link
Databases CAZY Describes the families of structurally related catalytic and carbohydrate binding modules (or functional domains) of enzymes that degrade, modify, or create glycosidic bonds
KEGG GLYCAN The KEGG GLYCAN structure database is a collection of experimentally determined glycan structures. It contains all unique structures taken from CarbBank, structures entered from recent publications, and structures present in KEGG
Glycan Library A list of approximately 830 lipid-linked sequence-defined glycan probes derived from diverse natural sources or chemically synthesized
GlycoMob An ion mobility-mass spectrometry collision cross-section database for glycomics
GlycoBase 3.2 A database of over 650 N- and O-linked glycan structures of HPLC, UPLC, exoglycosidase sequencing, and mass spectrometry (MALDI-MS, ESI-MS, ESI-MS/MS, LC-MS, LC-ESI-MS/MS) data
Glyco3D A portal of 3D structures of mono-, di-, oligo-, and polysaccharides and carbohydrate recognizing proteins (lectins, monoclonal antibodies, glycosyltransferases) and glycosaminoglycan binding proteins
GlyMAP An online resource mapping of the variational landscape of glycoactive enzymes A collection of databases and bioinformatics tools for glycobiology and glycomics
UniProtKB The universal protein sequence database with information on glycosylated proteins
UniCarbKB UniCarbKB is a curated and annotated glycan database which curates information from the scientific literature on glycoprotein-derived glycan structures. It includes data previously available from GlycoSuiteDB
UniCarbDB UniCarbDB is a platform for presenting glycan structures and fragment data characterized by LC-MS/MS strategies. The database is annotated with high-quality datasets and is designed to extend and reinforce those standards and ontologies developed by existing glycomics databases
UniPep A database for human N-linked glycosites: a resource for biomarker discovery
SugarBindDB SugarBindDB provides a collection of known carbohydrate sequences to which pathogenic organisms specifically adhere via lectins or adhesins. The data were compiled through an exhaustive search of literature published over the past 30 years by glycobiologists, microbiologists, and medical histologists
Consortium for Functional Glycomics (CFG) The CFG serves to combine the expertise and glycomics resources to reveal functions of glycans and glycan-binding proteins (GBPs) that impact human health and disease. The CFG offers resources to the community, including glycan array screening services, a reagent bank, and access to a large glycomics database and data analysis tools
GLYCONAVI A Website for carbohydrate research. It consists of the “GlycoNAVI database” for molecular information of carbohydrates, and chemical reactions of carbohydrate synthesis, the “Route Searching System for Glycan Synthesis,” and “GlycoNAVI tools” for editing two-dimensional molecular structure of carbohydrates
GlycoGeneDataBase (GGDB) Glycogene includes genes associated with glycan synthesis such as glycosyltransferase, sugar nucleotide synthases, sugar-nucleotide transporters, sulfotransferases, etc.
Carbohydrate Structure Data Base (CSDB) CSDB covers information on structures and taxonomy of natural carbohydrates published in the literature and mostly resolved by nuclear magnetic resonance (NMR). CSDB is composed of two parts: Bacterial and Archeal (BCSDB) and Plant and Fungal (PFCSDB)
EXPASy This section of the ExPASy server gathers a toolbox for processing data as well as simulating, predicting, or visualizing information, relative to glycans, glycoproteins, and glycan-binding proteins
TOOLS CASPER A tool for calculating NMR chemical shifts of oligo- and polysaccharides
Glycan Builder A software library and set of tools to allow the rapid drawing of glycan structures with support for all of the most common symbolic notation formats
GlycoDomainViewer An online resource to study site glycosylation with respect to protein context and conservation
Glynsight Glynsight offers visualization and interactive comparison of glycan expression profiles. The tool was initially developed with a focus on IgG N-glycan profiles but it was extended to usage with any experiment, which produces N- or O-linked glycan expression data
GlycoMinestruct A new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features
GlyMAP An online resource mapping out the variational landscape of glycoactive enzymes
GlycoMod An online tool to predict oligosaccharide structures on proteins from experimentally determined masses
GlycoMiner/GlycoPattern Software tools designed to detect, characterize, and perform relative quantitation of N-glycopeptides based on LC-MS runs A collection of databases and bioinformatics tools for glycobiology and glycomics
RINGS A Web resource providing algorithmic and data mining tools to aid glycobiology research
MonosaccharideDB A comprehensive reference source for monosaccharide notation
NetOGlyc Next generation prediction of O-glycosylation sites on proteins
GlycoSpectrumScan A Web-based bioinformatic tool designed to link glycomics and proteomics analyses for the characterization of glycopeptides. GlycoSpectrumScan is a MS platform which is independent, freely accessible, and profiles glycopeptide MS data using beforehand separately acquired released glycan and proteomics information. Both N- and O-glycosylated peptides as well as multiply glycosylated peptides can be analyzed
SimGlycan A predictive carbohydrate analysis tool for MS/MS data
SugarQb SugarQb enables genome-wide insights into protein glycosylation and glycan modifications in complex biological systems. This is a collection of software tools (Nodes) which enable the automated identification of intact glycopeptides from HCD‐MS/MS data sets, using commonly use peptide-centric MS/MS search engines
GlycoDigest GlycoDigest is a tool that simulates exoglycosidase digestion based on controlled rules acquired from expert knowledge and experimental evidence available in GlycoBase
Virtual Glycome This Website is focused on presenting selected computational tools and experimental resources that can be used to better understand the processes regulating cellular glycosylation at multiple levels
SweetUnityMol Software to display 3-D structures of carbohydrates, polysaccharides, and glycoconjugates

Table 1.

Tools and databases used in glycoinformatics.


6. Current bottlenecks for systems glycobiology

System-based analyses applied smoothly to network of signaling, metabolic processes, and physiological modeling; however, applications in systems glycobiology still have problems in computational and analytical studies and this situation arises from prominent bottlenecks [81]: (i) there is no accepted standard for model building; (ii) glycoinformatics databases are underdeveloped; (iii) and insufficient quantitative data are from glycoproteomics experiments.

In recent years, many systems based models have been developed to simulate biosynthesis of glycans. Nevertheless, difficulty in the incorporation of glycan structure and specificity data of enzymes related with glycosylation into mathematical models. As a result of this difficulty, systematic model building is still not present in this field. Moreover, limited number of the current models is available in Systems Biology Markup Language (SBML) format [82], which is the obstacle to develop, share, and validate computational models.

In the last decades, many databases related to glycoscience have emerged. Nevertheless, functional information is limited when compared to glycan structure and taxonomy data. In the future, relation of glycan structure to specific enzymes that synthesize them, the rates of their synthesis, and also their function are required in order to build model.

For the measurement of glycome, two main approaches are common. In the first approach, enzymes or mild hydrolysis is used to separate the glycans from the peptide backbone. Next, to obtain information about the composition and relative abundance of the carbohydrate structures, permethylation of glycans and MS analysis are used [83]. The bottleneck is the lack of well-developed software. For the data analysis of glycoproteomics and correspondingly acceleration of system-based model building and validation, more sophisticated computational tools are required.


7. Mathematical modeling of biochemical reaction networks

Mathematical models of glycosylation are developed in three main stages: (i) biological information gathering; (ii) model formulation; (iii) and simulation and postsimulation analysis. First step includes definition of components (enzyme, substrate, and product) crucial for the model. All of the components present in the biochemical network and connections between them are cataloged in this step. The process relies on information of biochemistry and cell biology, and analytical tools. In the next step, behavior in the steady state of the system is investigated by using simple linear algebra and principles of optimization. If time is a variable, the computer model can incorporate ordinary differential equations (ODEs) or Boolean networks. Proper kinetic/thermodynamic/stochastic/optimization parameters are collated depending on the formulation nature of the model and processes which are specified by enzymatically/nonenzymatically. The last step is performed to simulate the experimental system in the computer and to define unknown parameters of model by the help of fitting experimental data [81]. Visualization of multidimensional results is significant because numerous diverse models may attempt to fit one data set obtained from time labor and concentration-dependent experiments. As a result, consolidation of the findings obtained by simulations of complex reaction network and generation of hypotheses that can be tested experimentally require network analysis strategies.


8. Conclusions

Glycomics is a very comprehensive research area of science and interacts with several different omics fields. As many other omics types, it consists of a huge number of genomics components. In the future, techniques in high-throughput analysis and bioinformatics will be developed and enable the integration of all available data of glycomics into a particular diagram and by this way, it will be possible to develop biomarker and identify potential new therapeutic targets. Moreover, progresses in the field reveal that integrative multiomics approach should include glycomics in order to develop new biomarkers for robust diseases. One of the specific fields of systems biology is the systems glycobiology. It is based on a holistic approach that indicates process of complex glycosylation and associations between its constituents. A more complete glycome overview is targeted by using enzyme levels, abundances of glycans, pathways for biosynthesis, glycan annotation, and related omics data sets.

An approach of systems glycobiology is constructed in combination of various data sets of glycomics with that of other omics fields by the use of glycoinformatics tools to clarify understanding on process of glycosylation from various data sets. With the presented chapter, main aspects of glycobiology, glycomics, and systems glycobiology are summarized. However, these fields are still developing and further developments provide more insight to this specific research area.


  1. 1. Varki A. Biological roles of glycans. Glycobiology. 2017;27(1):3-491
  2. 2. Ohtsubo K, Marth JD. Glycosylation in cellular mechanisms of health and disease. Cell. 2006;126(5):855-867
  3. 3. York WS, Kochut KJ, Miller JA. Integration of Glycomics Knowledge and Data. Handbook of Glycomics. Amsterdam, The Netherlands: Elsevier; 2010. pp. 177-195
  4. 4. Ferreira CR, Turco L, Guimarães E, Saraiva SA, Bertolla RP, Perecin F, et al. Proteomics, metabolomics and lipidomics in reproductive biotechnologies: The MS solutions. Acta Scientiae Veterinariae. 2010;38:s591-s603
  5. 5. Varki A. Evolutionary forces shaping the Golgi glycosylation machinery: Why cell surface glycans are universal to living cells. Cold Spring Harbor Perspectives in Biology. 2011;3(6):a005462
  6. 6. Bennun SV, Hizal DB, Heffner K, Can O, Zhang H, Betenbaugh MJ. Systems glycobiology: Integrating glycogenomics, glycoproteomics, glycomics, and other ‘omics data sets to characterize cellular glycosylation processes. Journal of Molecular Biology. 2016;428(16):3337-3352
  7. 7. Yildiz SY, Erginer M, Demirci T, Hemberger J, Oner ET. Glycan-Based Nanocarriers in Drug Delivery. Drug Delivery Approaches and Nanosystems. Vol. 2. Florida (USA): Apple Academic Press; 2017. pp. 167-203
  8. 8. Panitch A, Paderi JE, Sharma S, Stuart KA, Vazquez-Portalatin NM. Extracellular Matrix-Binding Synthetic Peptidoglycans. IN (US): Google Patents; 2018
  9. 9. Dwek RA. Glycobiology: Toward understanding the function of sugars. Chemical Reviews. 1996;96(2):683-720
  10. 10. Gorelik E, Galili U, Raz A. On the role of cell surface carbohydrates and their binding proteins (lectins) in tumor metastasis. Cancer and Metastasis Reviews. 2001;20(3–4):245-277
  11. 11. Tommasone S, Allabush F, Tagger YK, Norman J, Köpf M, Tucker JH, et al. The challenges of glycan recognition with natural and artificial receptors. Chemical Society Reviews. 2019;48(22):5488-5505
  12. 12. Lau KS, Partridge EA, Grigorian A, Silvescu CI, Reinhold VN, Demetriou M, et al. Complex N-glycan number and degree of branching cooperate to regulate cell proliferation and differentiation. Cell. 2007;129(1):123-134
  13. 13. Tian Y, Zhang H. Glycoproteomics and clinical applications. Proteomics – Clinical Applications. 2010;4(2):124-132
  14. 14. Hwang H, Zhang J, Chung KA, Leverenz JB, Zabetian CP, Peskind ER, et al. Glycoproteomics in neurodegenerative diseases. Mass Spectrometry Reviews. 2010;29(1):79-125
  15. 15. Lowe JB, Marth JD. A genetic approach to mammalian glycan function. Annual Review of Biochemistry. 2003;72(1):643-691
  16. 16. Adamczyk B, Tharmalingam T, Rudd PM. Glycans as cancer biomarkers. Biochimica et Biophysica Acta (BBA) - General Subjects. 2012;1820(9):1347-1353
  17. 17. Hudak JE, Bertozzi CR. Glycotherapy: New advances inspire a reemergence of glycans in medicine. Chemistry & Biology. 2014;21(1):16-37
  18. 18. Lanctot PM, Gage FH, Varki AP. The glycans of stem cells. Current Opinion in Chemical Biology. 2007;11:373-380
  19. 19. Vasconcelos-dos-Santos A, Oliveira IA, Lucena MC, Mantuano NR, Whelan SA, Dias WB, et al. Biosynthetic machinery involved in aberrant glycosylation: Promising targets for developing of drugs against cancer. Frontiers in Oncology. 2015;5:138
  20. 20. Raman R, Raguram S, Venkataraman G, Paulson JC, Sasisekharan R. Glycomics: An integrated systems approach to structure-function relationships of glycans. Nature Methods. 2005;2(11):817
  21. 21. Liu L, Telford JE, Knezevic A, Rudd PM. High-Throughput Glycoanalytical Technology for Systems Glycobiology. London, UK: Portland Press Limited; 2010
  22. 22. Butler M, Quelhas D, Critchley AJ, Carchon H, Hebestreit HF, Hibbert RG, et al. Detailed glycan analysis of serum glycoproteins of patients with congenital disorders of glycosylation indicates the specific defective glycan processing step and provides an insight into pathogenesis. Glycobiology. 2003;13(9):601-622
  23. 23. Lauc G, Rudan I, Campbell H, Rudd PM. Complex genetic regulation of protein glycosylation. Molecular BioSystems. 2010;6(2):329-335
  24. 24. Soo EC, Hui JP. Metabolomics in glycomics. In: Functional Glycomics. Berlin, Germany: Springer; 2010. pp. 175-186
  25. 25. Zhang W, Li F, Nie L. Integrating multiple ‘omics’ analysis for microbial biology: Application and methodologies. Microbiology. 2010;156(2):287-301
  26. 26. Adua E, Russell A, Roberts P, Wang Y, Song M, Wang W. Innovation analysis on postgenomic biomarkers: Glycomics for chronic diseases. OMICS: A Journal of Integrative Biology. 2017;21(4):183-196
  27. 27. Ly M, Laremore TN, Linhardt RJ. Proteoglycomics: Recent progress and future challenges. OMICS: A Journal of Integrative Biology. 2010;14(4):389-399
  28. 28. Reily C, Stewart TJ, Renfrow MB, Novak J. Glycosylation in health and disease. Nature Reviews Nephrology. 2019;1
  29. 29. Gabius H-J, Kayser K. Introduction to glycopathology: The concept, the tools and the perspectives. Diagnostic Pathology. 2014;9(1):4
  30. 30. Kunej T. Rise of systems Glycobiology and personalized Glycomedicine: Why and how to integrate Glycomics with multiomics science? OMICS. 2019;23(12):615-622
  31. 31. Geissner A, Seeberger PH. Glycan arrays: From basic biochemical research to bioanalytical and biomedical applications. Annual Review of Analytical Chemistry. 2016;9:223-247
  32. 32. Smith DF, Cummings RD, Song X. History and future of shotgun glycomics. Biochemical Society Transactions. 2019;47(1):1-11
  33. 33. Narimatsu Y, Joshi HJ, Nason R, Van Coillie J, Karlsson R, Sun L, et al. An atlas of human glycosylation pathways enables display of the human glycome by gene engineered cells. Molecular Cell. 2019;75(2):394-407. e5
  34. 34. Geyer H, Geyer R. Strategies for analysis of glycoprotein glycosylation. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics. 2006;1764(12):1853-1869
  35. 35. Wilson IB. Molecular parasitology. In: Glycomics. Berlin, Germany: Springer; 2016. pp. 75-89
  36. 36. Alley WR Jr, Novotny MV. Structural glycomic analyses at high sensitivity: A decade of progress. Annual Review of Analytical Chemistry. 2013;6:237-265
  37. 37. Haslam SM, Morris HR, Dell A. Mass spectrometric strategies: Providing structural clues for helminth glycoproteins. Trends in Parasitology. 2001;17(5):231-235
  38. 38. Thaysen-Andersen M, Packer NH. Advances in LC–MS/MS-based glycoproteomics: Getting closer to system-wide site-specific mapping of the N-and O-glycoproteome. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics. 2014;1844(9):1437-1452
  39. 39. Schachter H. Biosynthetic controls that determine the branching and microheterogeneity of protein-bound oligosaccharides. Biochemistry and Cell Biology. 1986;64(3):163-181
  40. 40. Iskratsch T, Braun A, Paschinger K, Wilson IB. Specificity analysis of lectins and antibodies using remodeled glycoproteins. Analytical Biochemistry. 2009;386(2):133-146
  41. 41. Cummings RD, Pierce JM. The challenge and promise of glycomics. Chemistry & Biology. 2014;21(1):1-15
  42. 42. Ceroni A, Maass K, Geyer H, Geyer R, Dell A, Haslam SM. GlycoWorkbench: A tool for the computer-assisted annotation of mass spectra of glycans. Journal of Proteome Research. 2008;7(4):1650-1659
  43. 43. Maass K, Ranzinger R, Geyer H, von der Lieth CW, Geyer R. “Glyco-peakfinder”–De novo composition analysis of glycoconjugates. Proteomics. 2007;7(24):4435-4444
  44. 44. Goldberg D, Sutton-Smith M, Paulson J, Dell A. Automatic annotation of matrix-assisted laser desorption/ionization N-glycan spectra. Proteomics. 2005;5(4):865-875
  45. 45. Kawano S, Hashimoto K, Miyama T, Goto S, Kanehisa M, editors. Prediction of glycan structures from DNA microarray data. In: Glycobiology. NC, USA: Journals Department, Oxford University Press; 2004
  46. 46. An HJ, Lebrilla CB. A glycomics approach to the discovery of potential cancer biomarkers. In: Functional Glycomics. Berlin, Germany: Springer; 2010. pp. 199-213
  47. 47. Joshi HJ, Harrison MJ, Schulz BL, Cooper CA, Packer NH, Karlsson NG. Development of a mass fingerprinting tool for automated interpretation of oligosaccharide fragmentation data. Proteomics. 2004;4(6):1650-1664
  48. 48. Krambeck FJ, Betenbaugh MJ. A mathematical model of N-linked glycosylation. Biotechnology and Bioengineering. 2005;92(6):711-728
  49. 49. Bennun SV, Yarema KJ, Betenbaugh MJ, Krambeck FJ. Integration of the transcriptome and glycome for identification of glycan cell signatures. PLoS Computational Biology. 2013;9(1): e1002813
  50. 50. Kawano S, Hashimoto K, Miyama T, Goto S, Kanehisa M. Prediction of glycan structures from gene expression data based on glycosyltransferase reactions. Bioinformatics. 2005;21(21):3976-3982
  51. 51. Suga A, Yamanishi Y, Hashimoto K, Goto S, Kanehisa M. An improved scoring scheme for predicting glycan structures from gene expression data. Genome Informatics. 2007;18:237-246
  52. 52. Krambeck FJ, Bennun SV, Narang S, Choi S, Yarema KJ, Betenbaugh MJ. A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data. Glycobiology. 2009;19(11):1163-1175
  53. 53. Srinivasan S, Stephens C, Wilson E, Panchadsaram J, DeVoss K, Koistinen H, et al. Prostate cancer risk-associated single-nucleotide polymorphism affects prostate-specific antigen glycosylation and its function. Clinical Chemistry. 2019;65(1):e1-e9
  54. 54. Moehler TM, Seckinger A, Hose D, Andrulis M, Moreaux J, Hielscher T, et al. The glycome of normal and malignant plasma cells. PLoS One. 2013;8(12):e83719
  55. 55. Vojta A, Samaržija I, Bočkor L, Zoldoš V. Glyco-genes change expression in cancer through aberrant methylation. Biochimica et Biophysica Acta (BBA) - General Subjects. 2016;1860(8):1776-1785
  56. 56. Venturi G, Gomes Ferreira I, Pucci M, Ferracin M, Malagolini N, Chiricolo M, et al. Impact of sialyltransferase ST6GAL1 overexpression on different colon cancer cell types. Glycobiology. 2019;29(10):684-695
  57. 57. Saravanan C, Cao Z, Head SR, Panjwani N. Analysis of differential expression of glycosyltransferases in healing corneas by glycogene microarrays. Glycobiology. 2010;20(1):13-23
  58. 58. Barfeld SJ, East P, Zuber V, Mills IG. Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes. BMC Medical Genomics. 2014;7(1):513
  59. 59. Norton PA, Mehta AS. Expression of genes that control core fucosylation in hepatocellular carcinoma: Systematic review. World Journal of Gastroenterology. 2019;25(23):2947
  60. 60. Lauc G, Pezer M, Rudan I, Campbell H. Mechanisms of disease: The human N-glycome. Biochimica et Biophysica Acta (BBA) - General Subjects. 2016;1860(8):1574-1582
  61. 61. Azevedo R, Peixoto A, Gaiteiro C, Fernandes E, Neves M, Lima L, et al. Over forty years of bladder cancer glycobiology: Where do glycans stand facing precision oncology? Oncotarget. 2017;8(53):91734
  62. 62. Christiansen MN, Chik J, Lee L, Anugraham M, Abrahams JL, Packer NH. Cell surface protein glycosylation in cancer. Proteomics. 2014;14(4–5):525-546
  63. 63. Palmigiano A, Barone R, Sturiale L, Sanfilippo C, Bua RO, Romeo DA, et al. CSF N-glycoproteomics for early diagnosis in Alzheimer’s disease. Journal of Proteomics. 2016;131:29-37
  64. 64. Llop E, Guerrero PE, Duran A, Barrabés S, Massaguer A, Ferri MJ, et al. Glycoprotein biomarkers for the detection of pancreatic ductal adenocarcinoma. World Journal of Gastroenterology. 2018;24(24):2537
  65. 65. Brockhausen I, Yang JM, Burchell J, Whitehouse C, Taylor-Papadimitriou J. Mechanisms underlying aberrant glycosylation of MUC1 mucin in breast cancer cells. European Journal of Biochemistry. 1995;233(2):607-617
  66. 66. Gils A, Pedersen KE, Skottrup P, Christensen A, Naessens D, Deinum J, et al. Biochemical importance of glycosylation of plasminogen activator inhibitor-1. Thrombosis and Haemostasis. 2003;90(08):206-217
  67. 67. Lempiäinen A, Hotakainen K, Blomqvist C, Alfthan H, Stenman U-H. Hyperglycosylated human chorionic gonadotropin in serum of testicular cancer patients. Clinical Chemistry. 2012;58(7):1123-1129
  68. 68. Sato Y, Nakata K, Kato Y, Shima M, Ishii N, Koji T, et al. Early recognition of hepatocellular carcinoma based on altered profiles of alpha-fetoprotein. The New England Journal of Medicine. 1993;328(25):1802-1806
  69. 69. Gadroy P, Stridsberg M, Capon C, Michalski J-C, Strub J-M, van Dorsselaer A, et al. Phosphorylation and O-glycosylation sites of human chromogranin a (CGA79–439) from urine of patients with carcinoid tumors. The Journal of Biological Chemistry. 1998;273(51):34087-34097
  70. 70. Jankovic MM, Milutinovic BS. Glycoforms of CA125 antigen as a possible cancer marker. Cancer Biomarkers. 2008;4(1):35-42
  71. 71. Hua L, Liu Y, Zhen S, Wan D, Cao J, Gao X. Expression and biochemical characterization of recombinant human epididymis protein 4. Protein Expression and Purification. 2014;102:52-62
  72. 72. Konjevod M, Tudor L, Strac DS, Erjavec GN, Barbas C, Zarkovic N, et al. Metabolomic and glycomic findings in posttraumatic stress disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2019;88:181-193
  73. 73. Wang M, Yu G, Ressom HW. Integrative analysis of proteomic, glycomic, and metabolomic data for biomarker discovery. IEEE Journal of Biomedical and Health Informatics. 2016;20(5):1225-1231
  74. 74. An HJ, Kronewitter SR, de Leoz MLA, Lebrilla CB. Glycomics and disease markers. Current Opinion in Chemical Biology. 2009;13(5–6):601-607
  75. 75. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: The human metabolome database. Nucleic Acids Research. 2007;35(suppl_1):D521-D5D6
  76. 76. Furukawa K, Ohmi Y, Ohkawa Y, Bhuiyan RH, Zhang P, Tajima O, et al. New era of research on cancer-associated glycosphingolipids. Cancer Science. 2019;110(5):1544
  77. 77. Dehelean L, Sarbu M, Petrut A, Zamfir AD. Trends in glycolipid biomarker discovery in neurodegenerative disorders by mass spectrometry. In: Advancements of Mass Spectrometry in Biomedical Research. Springer; 2019. pp. 703-729
  78. 78. Kitov PI, Kitova EN, Han L, Li Z, Jung J, Rodrigues E, et al. A quantitative, high-throughput method identifies protein–glycan interactions via mass spectrometry. Communications Biology. 2019;2(1):1-7
  79. 79. Bonnardel F, Mariethoz J, Salentin S, Robin X, Schroeder M, Perez S, et al. UniLectin3D, a database of carbohydrate binding proteins with curated information on 3D structures and interacting ligands. Nucleic Acids Research. 2019;47(D1):D1236-D1D44
  80. 80. AbuSamra DB, Argüeso P. Lectin-glycan interactions in corneal infection and inflammation. Frontiers in Immunology. 2018;9:2338
  81. 81. Liu G, Neelamegham S. Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data. Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2015;7(4):163-181
  82. 82. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, et al. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics. 2003;19(4):524-531
  83. 83. Mondal N, Buffone A Jr, Stolfa G, Antonopoulos A, Lau JT, Haslam SM, et al. ST3Gal-4 is the primary sialyltransferase regulating the synthesis of E-, P-, and L-selectin ligands on human myeloid leukocytes. Blood: The Journal of the American Society of Hematology. 2015;125(4):687-696

Written By

Songül Yaşar Yıldız

Submitted: 01 November 2019 Reviewed: 25 March 2020 Published: 02 May 2020