Prediction of glycan structures from gene expression data based on glycosyltransferase reactions

Integration of RNAseq transcriptomics and N-glycomics reveal biosynthetic pathways and predict structure-specific N-glycan expression

The processes involved in protein N-glycosylation represent new therapeutic targets for diseases but their stepwise and overlapping biosynthetic processes make it challenging to identify the specific glycogenes involved. In this work, we aimed to elucidate the interactions between glycogene expression and N-glycan abundance by constructing supervised machine-learning models for each N-glycan composition. Regression models were trained to predict N-glycan abundance (response variable) from glycogene expression (predictors) using paired LC-MS/MS N-glycomic and 3'-TagSeq transcriptomic datasets from cells derived from multiple tissue origins and treatment conditions. The datasets include cells from several tissue origins - B cell, brain, colon, lung, muscle, prostate - encompassing nearly 400 N-glycan compounds and over 160 glycogenes filtered from an 18 000-gene transcriptome. Accurate models (validation R 2 > 0.8) predicted N-glycan abundance across cell types, including GLC01 (lung cancer), CCD19-Lu (lung fibroblast), and Tib-190 (B cell). Model importance scores ranked glycogene contributions to N-glycan predictions, revealing significant glycogene associations with specific N-glycan types. The predictions were consistent across input cell quantities, unlike LC-MS/MS glycomics which showed inconsistent results. This suggests that the models can reliably predict N-glycosylation even in samples with low cell amounts and by extension, single-cell samples. These findings can provide insights into cellular N-glycosylation machinery, offering potential therapeutic strategies for diseases linked to aberrant glycosylation, such as cancer, and neurodegenerative and autoimmune disorders.

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

Informatics Ecosystems to Advance the Biology of Glycans

Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential and has exhibited initial success, to extend the research community to include biologists, clinicians, chemists, and computer scientists. This chapter discusses the technology and approach needed to create integrated data resources and informatics ecosystems to empower the broader community to leverage extant glycomics data. The focus is on glycosaminoglycan (GAGs) and proteoglycan research, but the approach can be generalized. The methods described span the development of glycomics standards from CarbBank to Glyco Connection Tables. Integrated data sets provide a foundation for novel methods of analysis such as machine learning and deep learning for knowledge discovery. The implications of predictive analysis are examined in relation to disease biomarker to expand the target audience of GAG and proteoglycan research.

Systems Glycobiology: Past, Present, and Future

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.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

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.

Omics-based Identification of Glycan Structures as Biomarkers for a Variety of Diseases

Glycans play important roles in cell communication, protein interaction, and immunity, and structural changes in glycans are associated with the regulation of a range of biological pathways involved in disease. However, our understanding of the detailed relationships between specific diseases and glycans is very limited. In this study, we proposed an omics-based method to investigate the correlations between glycans and a wide range of human diseases. We analyzed the gene expression patterns of glycogenes (glycosyltransferases and glycosidases) for 79 different diseases. A biological pathway-based glycogene signature was constructed to identify the alteration in glycan biosynthesis and the associated glycan structures for each disease state. The degradation of N-glycan and keratan sulfate, for example, may promote the growth or metastasis of multiple types of cancer, including endometrial, gastric, and nasopharyngeal. Our results also revealed that commonalities between diseases can be interpreted using glycogene expression patterns, as well as the associated glycan structure patterns at the level of the affected pathway. The proposed method is expected to be useful for understanding the relationships between glycans, glycogenes, and disease and identifying disease-specific glycan biomarkers.

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 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.

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.

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.

Two-Dimensional N-Glycan Distribution Mapping of Hepatocellular Carcinoma Tissues by MALDI-Imaging Mass Spectrometry

A new mass spectrometry imaging approach to simultaneously map the two-dimensional distribution of N-glycans in tissues has been recently developed. The method uses Matrix Assisted Laser Desorption Ionization Imaging Mass Spectrometry (MALDI-IMS) to spatially profile the location and distribution of multiple N-linked glycan species released by peptide N-glycosidase F in frozen or formalin-fixed tissues. Multiple formalin-fixed human hepatocellular carcinoma tissues were evaluated with this method, resulting in a panel of over 30 N-glycans detected. An ethylation reaction of extracted N-glycans released from adjacent slides was done to stabilize sialic acid containing glycans, and these structures were compared to N-glycans detected directly from tissue profiling. In addition, the distribution of singly fucosylated N-glycans detected in tumor tissue microarray cores were compared to the histochemistry staining pattern of a core fucose binding lectin. As this MALDI-IMS workflow has the potential to be applied to any formalin-fixed tissue block or tissue microarray, the advantages and limitations of the technique in context with other glycomic methods are also summarized.

Glycomic analysis using KEGG GLYCAN

This chapter describes the KEGG GLYCAN database of the KEGG resource, including descriptions of links to the other databases in KEGG. In particular, KEGG GLYCAN consists of glycan structures, with links to glycogenes, orthologs, reactions, pathways, drugs, diseases, and others, all within the KEGG resources. A number of analytical tools are also available, including the composite structure map (CSM), KegDraw, KCam, and GECS. These databases and tools will be described along with simple examples of their usage.

Informatics tools to advance the biology of glycosaminoglycans and proteoglycans

Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential to extend the research community to include biologists, clinicians, chemists, and computer scientists. This chapter discusses the technology and approach needed to create integrated data resources to empower the broader community to leverage extant glycomics data. The focus is on glycosaminoglycan (GAGs) and proteoglycan research, but the approach can be generalized. The methods described span the development of glycomics standards from CarbBank to Glyco Connection Tables. The existence of integrated data sets provides a foundation for novel methods of analysis such as machine learning for knowledge discovery. The implications of predictive analysis are examined in relation to disease biomarker to expand the target audience of GAG and proteoglycan research.

Systems glycobiology for glycoengineering

Glycosylation serves essential functions on many proteins produced in biopharmaceutical manufacturing, making it mandatory to thoroughly consider its biogenesis during the production process. Glycoengineering efforts involve the rational design of glycosylation through adjustments in culturing conditions or genetic modifications. Computational models have been developed to aid this process, aiming to offer cheaper and faster alternatives to costly screening strategies. Recently, these models have been successfully utilized to predict glycosylation of products of industrial relevance. Furthermore, systems-level analyses of glycan diversity are elucidating deeper insights into the mechanisms underlying glycosylation. As computational models of glycosylation continue to be expanded, refined, and leveraged for detailed analysis of glycomics data, they will become invaluable resources for cell line development and glycoengineering.

Deciphering the glycogenome of schistosomes

Schistosoma mansoni and other Schistosoma sp. are multicellular parasitic helminths (worms) that infect humans and mammals worldwide. Infection by these parasites, which results in developmental maturation and sexual differentiation of the worms over a period of 5–6 weeks, induces antibodies to glycan antigens expressed in surface and secreted glycoproteins and glycolipids. There is growing interest in defining these unusual parasite-synthesized glycan antigens and using them to understand immune responses, their roles in immunomodulation, and in using glycan antigens as potential vaccine targets. A key problem in this area, however, has been the lack of information about the enzymes involved in elaborating the complex repertoire of glycans represented by the schistosome glycome. Recent availability of the nuclear genome sequences for Schistosoma sp. has created the opportunity to define the glycogenome, which represents the specific genes and cognate enzymes that generate the glycome. Here we describe the current state of information in regard to the schistosome glycogenome and glycome and highlight the important classes of glycans and glycogenes that may be important in their generation.

The sweets standing at the borderline between allo- and xenotransplantation

Animal cells are densely covered with glycoconjugates, such as N-glycan, O-glycan, and glycosphingolipids, which are important for various biological and immunological events at the cell surface and in the extracellular matrix. Endothelial α-Gal carbohydrate epitopes (Galα3Gal-R) expressed on porcine tissue or cell surfaces are such glycoconjugates and directly mediate hyperacute immunological rejection in pig-to-human xenotransplantation. Although researchers have been able to develop α1,3-galactosyltransferase (GalT) gene knockout (KO) pigs, there remain unclarified non-Gal antigens that prevent xenotransplantation. Based on our expertise in the structural analysis of xenoantigenic carbohydrates, we describe the immunologically significant non-human carbohydrate antigens, including α-Gal antigens, analyzed as part of efforts to assess the antigens responsible for hyperacute immunological rejection in pig-to-human xenotransplantation. The importance of studying human, pig, and GalT-KO pig glycoprofiles, and of developing adequate pig-to-human glycan databases, is also discussed.

Chemoinformatics on Metabolic Pathways

Chemical genomics is one of the cutting-edge research areas in the post-genomic era, which requires a sophisticated integration of heterogeneous information, i.e., genomic and chemical information. Enzymes play key roles for dynamic behavior of living organisms, linking information in the chemical space and genomic space. In this chapter, the authors report our recent efforts in this area, including the development of a similarity measure between two chemical compounds, a prediction system of a plausible enzyme for a given substrate and product pair, and two different approaches to predict the fate of a given compound in a metabolic pathway. General problems and possible future directions are also discussed, in hope to attract more activities from many researchers in this research area.

Using databases and web resources for glycomics research

Many databases of carbohydrate structures and related information can be found on the World Wide Web. This review covers the major carbohydrate databases that have potential utility for glycoscientists and researchers entering the glycosciences. The first half provides a brief overview of carbohydrate databases and web resources (including a history of carbohydrate databases and carbohydrate notations used in these databases), and the second half provides a guide that can be used as an index to determine which resources provide the data of most interest to the user.

Integration of the transcriptome and glycome for identification of glycan cell signatures

Abnormalities in glycan biosynthesis have been conclusively linked to many diseases but the complexity of glycosylation has hindered the analysis of glycan data in order to identify glycoforms contributing to disease. To overcome this limitation, we developed a quantitative N-glycosylation model that interprets and integrates mass spectral and transcriptomic data by incorporating key glycosylation enzyme activities. Using the cancer progression model of androgen-dependent to androgen-independent Lymph Node Carcinoma of the Prostate (LNCaP) cells, the N-glycosylation model identified and quantified glycan structural details not typically derived from single-stage mass spectral or gene expression data. Differences between the cell types uncovered include increases in H(II) and Le(y) epitopes, corresponding to greater activity of α2-Fuc-transferase (FUT1) in the androgen-independent cells. The model further elucidated limitations in the two analytical platforms including a defect in the microarray for detecting the GnTV (MGAT5) enzyme. Our results demonstrate the potential of systems glycobiology tools for elucidating key glycan biomarkers and potential therapeutic targets. The integration of multiple data sets represents an important application of systems biology for understanding complex cellular processes.

The use of glycoinformatics in glycochemistry

Glycoinformatics is a small but growing branch of bioinformatics and chemoinformatics. Various resources are now available that can be of use to glycobiologists, but also to chemists who work on the synthesis or analysis of carbohydrates. This article gives an overview of existing glyco-specific databases and tools, with a focus on their application to glycochemistry: Databases can provide information on candidate glycan structures for synthesis, or on glyco-enzymes that can be used to synthesize carbohydrates. Statistical analyses of glycan databases help to plan glycan synthesis experiments. 3D-Structural data of protein-carbohydrate complexes are used in targeted drug design, and tools to support glycan structure analysis aid with quality control. Specific problems of glycoinformatics compared to bioinformatics for genomics or proteomics, especially concerning integration and long-term maintenance of the existing glycan databases, are also discussed.

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein-carbohydrate interaction are reviewed.

Cell migration-the role of integrin glycosylation

BACKGROUND

Cell migration is an essential process in organ homeostasis, in inflammation, and also in metastasis, the main cause of death from cancer. The extracellular matrix (ECM) serves as the molecular scaffold for cell adhesion and migration; in the first phase of migration, adhesion of cells to the ECM is critical. Engagement of integrin receptors with ECM ligands gives rise to the formation of complex multiprotein structures which link the ECM to the cytoplasmic actin skeleton. Both ECM proteins and the adhesion receptors are glycoproteins, and it is well accepted that N-glycans modulate their conformation and activity, thereby affecting cell-ECM interactions. Likely targets for glycosylation are the integrins, whose ability to form functional dimers depends upon the presence of N-linked oligosaccharides. Cell migratory behavior may depend on the level of expression of adhesion proteins, and their N-glycosylation that affect receptor-ligand binding.

SCOPE OF REVIEW

The mechanism underlying the effect of integrin glycosylation on migration is still unknown, but results gained from integrins with artificial or mutated N-glycosylation sites provide evidence that integrin function can be regulated by changes in glycosylation.

GENERAL SIGNIFICANCE

A better understanding of the molecular mechanism of cell migration processes could lead to novel diagnostic and therapeutic approaches and applications. For this, the proteins and oligosaccharides involved in these events need to be characterized.

A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data

Effective representation and characterization of biosynthetic pathways of glycosylation can be facilitated by mathematical modeling. This paper describes the expansion of a previously developed detailed model for N-linked glycosylation with the further application of the model to analyze MALDI-TOF mass spectra of human N-glycans in terms of underlying cellular enzyme activities. The glycosylation reaction network is automatically generated by the model, based on the reaction specificities of the glycosylation enzymes. The use of a molecular mass cutoff and a network pruning method typically limits the model size to about 10,000 glycan structures. This allows prediction of the complete glycan profile and its abundances for any set of assumed enzyme concentrations and reaction rate parameters. A synthetic mass spectrum from model-calculated glycan profiles is obtained and enzyme concentrations are adjusted to bring the theoretically calculated mass spectrum into agreement with experiment. The result of this process is a complete characterization of a measured glycan mass spectrum containing hundreds of masses in terms of the activities of 19 enzymes. In addition, a complete annotation of the mass spectrum in terms of glycan structure is produced, including the proportions of isomers within each peak. The method was applied to mass spectrometric data of normal human monocytes and monocytic leukemia (THP1) cells to derive glycosyltransferase activity changes underlying the differences in glycan structure between the normal and diseased cells. Model predictions could lead to a better understanding of the changes associated with disease states, identification of disease-associated biomarkers, and bioengineered glycan modifications.

Glycome-DB.org: a portal for querying across the digital world of carbohydrate sequences

Despite ongoing harmonization efforts, the major carbohydrate sequence databases following the first initiative in this field, CarbBank, are still isolated islands, with mechanisms for automatic structure exchange and comparison largely missing. This unfavorable situation has been overcome with a systematic data integration effort, resulting in the GlycomeDB, a meta-database for public carbohydrate sequences. It contains at present 35,056 unique structures in GlycoCT encoding, referencing more than 100,000 external records from 1845 different taxonomic sources. We have created a user-friendly, web-based graphical interface which allows taxonomic and structural data to be entered and searched for. The structural search possibilities include substructure search, similarity search, and maximum common substructure. A novel search refinement mechanism allows the assembly of complex queries. With GlycomeDB (www.glycome-db.org), it is now possible to use a single portal to access all digitally encoded, public structural data in glycomics and to perform complex queries with the help of a web-based user interface.

Systems-level studies of glycosyltransferase gene expression and enzyme activity that are associated with the selectin binding function of human leukocytes

The application of systems biology methods in the emerging field of glycomics requires the collection and integration of glycosyltransferase data at the gene and enzyme level for the purpose of hypothesis generation. We systematically examined the relationship between gene expression, glycosyltransferase activity, glycan expression, and selectin-binding function in different systems, including human neutrophils, undifferentiated HL-60 (human promyelocytic cells), differentiated HL-60, and HL-60 synchronized in specific growth phases. Results demonstrate that 1) the sLe(X) (sialyl-Lewis-X) epitope is expressed in P-selectin glycoprotein ligand-1 (PSGL-1) from neutrophils at higher levels compared with HL-60. This variation may be due to differences in the relative activities of alpha1,3-fucosyltransferases and alpha2,3-sialyltransferases in these two cell types. 2) HL-60 cell differentiation along granulocyte lineage increased the activity of beta1,4GalT and beta1,3GlcNAcT by 1.6- to 3.2-fold. This may contribute to LacNAc chain extension as evidenced by the 1.7-fold increase in DSA-lectin (lectin recognizing LacNAc) binding to cells after differentiation. 3) The activity of enzymes contributing to sLe(X) formation in leukocytes likely varies as ST3[Galbeta1,4GlcNAc] < or = alpha1,3FT[sialyl-LacNAc] < beta1,3GlcNAcT. 4) O-glycan specific glycosyltransferase activity does not undergo periodic variation with cell cycle phases. Overall, gene expression and enzyme activity data combined with knowledge of biochemistry can predict the resulting glycan structures and yield viable experimentally testable hypothesis.

Statistical analysis of the Bacterial Carbohydrate Structure Data Base (BCSDB): characteristics and diversity of bacterial carbohydrates in comparison with mammalian glycans

Background

There are considerable differences between bacterial and mammalian glycans. In contrast to most eukaryotic carbohydrates, bacterial glycans are often composed of repeating units with diverse functions ranging from structural reinforcement to adhesion, colonization and camouflage. Since bacterial glycans are typically displayed at the cell surface, they can interact with the environment and, therefore, have significant biomedical importance.

Results

The sequence characteristics of glycans (monosaccharide composition, modifications, and linkage patterns) for the higher bacterial taxonomic classes have been examined and compared with the data for mammals, with both similarities and unique features becoming evident. Compared to mammalian glycans, the bacterial glycans deposited in the current databases have a more than ten-fold greater diversity at the monosaccharide level, and the disaccharide pattern space is approximately nine times larger. Specific bacterial subclasses exhibit characteristic glycans which can be distinguished on the basis of distinctive structural features or sequence properties.

Conclusion

For the first time a systematic database analysis of the bacterial glycome has been performed. This study summarizes the current knowledge of bacterial glycan architecture and diversity and reveals putative targets for the rational design and development of therapeutic intervention strategies by comparing bacterial and mammalian glycans.

Predicting sub-Golgi localization of type II membrane proteins

Motivation: Recent research underlines the importance of finegrained knowledge on protein localization. In particular, subcompartmental localization in the Golgi apparatus is important, for example, for the order of reactions performed in glycosylation pathways or the sorting functions of SNAREs, but is currently poorly understood.

Results: We assemble a dataset of type II transmembrane proteins with experimentally determined sub-Golgi localizations and use this information to develop a predictor based on the transmembrane domain of these proteins, making use of a dedicated proteinstructure based kernel in an SVM. Various applications demonstrate the power of our approach. In particular, comparison with a large set of glycan structures illustrates the applicability of our predictions on a ‘glycomic’ scale and demonstrates a significant correlation between sub-Golgi localization and the ordering of different steps in glycan biosynthesis.

Contact:roeland.vanham@wur.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

(Glyco)sphingolipidology: an amazing challenge and opportunity for systems biology

Sphingolipids are found in essentially all eukaryotes and in some prokaryotes and viruses, where they influence cell structure, signaling and interactions with the extracellular environment. Because of the combinatorial nature of their biosynthesis, the sphingolipidome comprises untold thousands of species that encompass bioactive backbones and complex phospho- and glycolipids. Mass spectrometry is able to analyze a growing fraction of the sphingolipidome and is beginning to provide information about localization. Use of these structure specific, quantitative methods is producing insights, and surprises, regarding sphingolipid structure, metabolism, function and disease. Dealing with such large data sets poses special challenges for systems biology, but the intrinsic and elegant interrelationships among these compounds might provide a key to dealing with the complexity of the sphingolipidome.

KEGG for linking genomes to life and the environment

KEGG (http://www.genome.jp/kegg/) is a database of biological systems that integrates genomic, chemical and systemic functional information. KEGG provides a reference knowledge base for linking genomes to life through the process of PATHWAY mapping, which is to map, for example, a genomic or transcriptomic content of genes to KEGG reference pathways to infer systemic behaviors of the cell or the organism. In addition, KEGG provides a reference knowledge base for linking genomes to the environment, such as for the analysis of drug-target relationships, through the process of BRITE mapping. KEGG BRITE is an ontology database representing functional hierarchies of various biological objects, including molecules, cells, organisms, diseases and drugs, as well as relationships among them. KEGG PATHWAY is now supplemented with a new global map of metabolic pathways, which is essentially a combined map of about 120 existing pathway maps. In addition, smaller pathway modules are defined and stored in KEGG MODULE that also contains other functional units and complexes. The KEGG resource is being expanded to suit the needs for practical applications. KEGG DRUG contains all approved drugs in the US and Japan, and KEGG DISEASE is a new database linking disease genes, pathways, drugs and diagnostic markers.

Correlation index-based responsible-enzyme gene screening (CIRES), a novel DNA microarray-based method for enzyme gene involved in glycan biosynthesis

BACKGROUND

Glycan biosynthesis occurs though a multi-step process that requires a variety of enzymes ranging from glycosyltransferases to those involved in cytosolic sugar metabolism. In many cases, glycan biosynthesis follows a glycan-specific, linear pathway. As glycosyltransferases are generally regulated at the level of transcription, assessing the overall transcriptional profile for glycan biosynthesis genes seems warranted. However, a systematic approach for assessing the correlation between glycan expression and glycan-related gene expression has not been reported previously.

METHODOLOGY

To facilitate genetic analysis of glycan biosynthesis, we sought to correlate the expression of genes involved in cell-surface glycan formation with the expression of the glycans, as detected by glycan-recognizing probes. We performed cross-sample comparisons of gene expression profiles using a newly developed, glycan-focused cDNA microarray. Cell-surface glycan expression profiles were obtained using flow cytometry of cells stained with plant lectins. Pearson's correlation coefficients were calculated for these profiles and were used to identify enzyme genes correlated with glycan biosynthesis.

CONCLUSIONS

This method, designated correlation index-based responsible-enzyme gene screening (CIRES), successfully identified genes already known to be involved in the biosynthesis of certain glycans. Our evaluation of CIRES indicates that it is useful for identifying genes involved in the biosynthesis of glycan chains that can be probed with lectins using flow cytometry.

Predicted functions and linkage specificities of the products of the Streptococcus pneumoniae capsular biosynthetic loci

The sequences of the capsular biosynthetic (cps) loci of 90 serotypes of Streptococcus pneumoniae have recently been determined. Bioinformatic procedures were used to predict the general functions of 1,973 of the 1,999 gene products and to identify proteins within the same homology group, Pfam family, and CAZy glycosyltransferase family. Correlating cps gene content with the 54 known capsular polysaccharide (CPS) structures provided tentative assignments of the specific functions of the different homology groups of each functional class (regulatory proteins, enzymes for synthesis of CPS constituents, polymerases, flippases, initial sugar transferases, glycosyltransferases [GTs], phosphotransferases, acetyltransferases, and pyruvyltransferases). Assignment of the glycosidic linkages catalyzed by the 342 GTs (92 homology groups) is problematic, but tentative assignments could be made by using this large set of cps loci and CPS structures to correlate the presence of particular GTs with specific glycosidic linkages, by correlating inverting or retaining linkages in CPS repeat units with the inverting or retaining mechanisms of the GTs predicted from their CAZy family membership, and by comparing the CPS structures of serotypes that have very similar cps gene contents. These large-scale comparisons between structure and gene content assigned the linkages catalyzed by 72% of the GTs, and all linkages were assigned in 32 of the serotypes with known repeat unit structures. Clear examples where very similar initial sugar transferases or glycosyltransferases catalyze different linkages in different serotypes were also identified. These assignments should provide a stimulus for biochemical studies to evaluate the reactions that are proposed.

Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways

The roles of chemical compounds in biological systems are now systematically analyzed by high-throughput experimental technologies. To automate the processing and interpretation of large-scale data it is necessary to develop bioinformatics methods to extract information from the chemical structures of these small molecules by considering the interactions and reactions involving proteins and other biological macromolecules. Here we focus on metabolic compounds and present a knowledge-based approach for understanding reactivity and metabolic fate in enzyme-catalyzed reactions in a given organism or group. We first constructed the KEGG RPAIR database containing chemical structure alignments and structure transformation patterns, called RDM patterns, for 7091 reactant pairs (substrate-product pairs) in 5734 known enzyme-catalyzed reactions. A total of 2205 RDM patterns were then categorized based on the KEGG PATHWAY database. The majority of RDM patterns were uniquely or preferentially found in specific classes of pathways, although some RDM patterns, such as those involving phosphorylation, were ubiquitous. The xenobiotics biodegradation pathways contained the most distinct RDM patterns, and we developed a scheme for predicting bacterial biodegradation pathways given chemical structures of, for example, environmental compounds.

Annotating the human proteome: Beyond establishing a parts list

The completion of the human genome has shifted the attention from deciphering the sequence to the identification and characterisation of the functional components, including genes. Improved gene prediction algorithms, together with the existing transcript and protein information, have enabled the identification of most exons in a genome. Availability of the 'parts list' has fostered the development of experimental approaches to systematically interrogate gene function on the genome, transcriptome and proteome level. Studying gene function at the protein level is vital to the understanding of how cells perform their functions as variations in protein isoforms and protein quantity which may underlie a change in phenotype can often not be deduced from sequence or transcript level genomics experiments alone. Recent advancements in proteomics have afforded technologies capable of measuring protein expression, post-translational modifications of these proteins, their subcellular localisation and assembly into complexes and pathways. Although an enormous amount of data already exists on the function of many human proteins, much of it is scattered over multiple resources. Public domain databases are therefore required to manage and collate this information and present it to the user community in both a human and machine readable manner. Of special importance here is the integration of heterogeneous data to facilitate the creation of resources that go beyond a mere parts list.

Activation of murine CD4+ and CD8+ T lymphocytes leads to dramatic remodeling of N-linked glycans

Differentiation and activation of lymphocytes are documented to result in changes in glycosylation associated with biologically important consequences. In this report, we have systematically examined global changes in N-linked glycosylation following activation of murine CD4 T cells, CD8 T cells, and B cells by MALDI-TOF mass spectrometry profiling, and investigated the molecular basis for those changes by assessing alterations in the expression of glycan transferase genes. Surprisingly, the major change observed in activated CD4 and CD8 T cells was a dramatic reduction of sialylated biantennary N-glycans carrying the terminal NeuGcalpha2-6Gal sequence, and a corresponding increase in glycans carrying the Galalpha1-3Gal sequence. This change was accounted for by a decrease in the expression of the sialyltransferase ST6Gal I, and an increase in the expression of the galactosyltransferase, alpha1-3GalT. Conversely, in B cells no change in terminal sialylation of N-linked glycans was evident, and the expression of the same two glycosyltransferases was increased and decreased, respectively. The results have implications for differential recognition of activated and unactivated T cells by dendritic cells and B cells expressing glycan-binding proteins that recognize terminal sequences of N-linked glycans.

Advancing glycomics: implementation strategies at the consortium for functional glycomics

Glycomics-an integrated approach to study structure-function relationships of complex carbohydrates (or glycans)-is an emerging field in this age of post-genomics. Realizing the importance of glycomics, many large scale research initiatives have been established to generate novel resources and technologies to advance glycomics. These initiatives are generating and cataloging diverse data sets necessitating the development of bioinformatic platforms to acquire, integrate, and disseminate these data sets in a meaningful fashion. With the consortium for functional glycomics (CFG) as the model system, this review discusses databases and the bioinformatics platform developed by this consortium to advance glycomics.