Field Of Glycomics Research Articles

Glycoprofiling of cancer biomarkers: Label-free electrochemical lectin-based biosensors.

Glycosylation of biomolecules is one of the most prevalent post- and co-translational modification in a human body, with more than half of all human proteins being glycosylated. Malignant transformation of cells influences glycosylation machinery resulting in subtle changes of the glycosylation pattern within the cell populations as a result of cancer. Thus, an altered terminal glycan motif on glycoproteins could provide a warning signal about disease development and progression and could be applied as a reliable biomarker in cancer diagnostics. Among all highly effective glycoprofiling tools, label-free electrochemical impedance spectroscopy (EIS)-based biosensors have emerged as especially suitable tool for point-of-care early-stage cancer detection. Herein, we highlight the current challenges in glycoprofiling of various cancer biomarkers by ultrasensitive impedimetric-based biosensors with low sample consumption, low cost fabrication and simple miniaturization. Additionally, this review provides a short introduction to the field of glycomics and lectinomics and gives a brief overview of glycan alterations in different types of cancer.

Glycosylation characteristics of colorectal cancer.

Glycans on proteins and lipids are known to alter with malignant transformation. The study of these may contribute to the discovery of biomarkers and treatment targets as well as understanding of cancer biology. We here describe the change of glycosylation specifically defining colorectal cancer with view on N-glycans, O-glycans, and glycosphingolipid glycans in colorectal cancer cells and tissues as well as patient sera. Glycan alterations observed in colon cancer include increased β1,6-branching and correlating higher abundance of (poly-)N-acetyllactosamine extensions of N-glycans as well as an increase in (truncated) high-mannose type glycans, while bisected structures decrease. Colorectal cancer-associated O-glycan changes are predominated by reduced expression of core 3 and 4 glycans, whereas higher levels of core 1 glycans, (sialyl) T-antigen, (sialyl) Tn-antigen, and a generally higher density of O-glycans are observed. Specific changes for glycosphingolipid glycans are lower abundances of disialylated structures as well as globo-type glycosphingolipid glycans with exception of Gb3. In general, alterations affecting all discussed glycan types are increased sialylation, fucosylation as well as (sialyl) Lewis-type antigens and type-2 chain glycans. As a consequence, interactions with glycan-binding proteins can be affected and the biological function and cellular consequences of the altered glycosylation with regard to tumorigenesis, metastasis, modulation of immunity, and resistance to antitumor therapy will be discussed. Finally, analytical approaches aiding in the field of glycomics will be reviewed with focus on binding assays and mass spectrometry.

Multidimensional fractionation is a requirement for quantitation of Golgi-resident glycosylation enzymes from cultured human cells.

Glycosylation results from the concerted action of glycosylation enzymes in the secretory pathway. In general, gene expression serves as the primary control mechanism, but post-translational fine-tuning of glycosylation enzyme functions is often necessary for efficient synthesis of specific glycan epitopes. While the field of glycomics has rapidly advanced, there lacks routine proteomic methods to measure expression of specific glycosylation enzymes needed to fill the gap between mRNA expression and the glycomic profile in a "reverse genomics" workflow. Toward developing this workflow we enriched Golgi membranes from two human colon cancer cell lines by sucrose density centrifugation and further mass-based fractionation by SDS-PAGE. We then applied mass spectrometry to demonstrate a doubling in the number of Golgi resident proteins identified, compared to the unenriched, low speed centrifuged supernatant of lysed cells. A total of 35 Golgi-resident glycosylation enzymes, of which 23 were glycosyltransferases, were identified making this the largest protein database so far of Golgi resident glycosylation enzymes experimentally identified in cultured human cells. We developed targeted mass spectrometry assays for specific quantitation of many of these glycosylation enzymes. Our results show that alterations in abundance of glycosylation enzymes at the protein level were generally consistent with the resultant glycomic profiles, but not necessarily with the corresponding glycosyltransferase mRNA expression as exemplified by the case of O-glycan core 1 T synthase.

GlycoRDF: an ontology to standardize glycomics data in RDF.

Over the last decades several glycomics-based bioinformatics resources and databases have been created and released to the public. Unfortunately, there is no common standard in the representation of the stored information or a common machine-readable interface allowing bioinformatics groups to easily extract and cross-reference the stored information. An international group of bioinformatics experts in the field of glycomics have worked together to create a standard Resource Description Framework (RDF) representation for glycomics data, focused on glycan sequences and related biological source, publications and experimental data. This RDF standard is defined by the GlycoRDF ontology and will be used by database providers to generate common machine-readable exports of the data stored in their databases. The ontology, supporting documentation and source code used by database providers to generate standardized RDF are available online (http://www.glycoinfo.org/GlycoRDF/).

Applications of Multiple Reaction Monitoring to Clinical Glycomics.

Multiple reaction monitoring or MRM is widely acknowledged for its accuracy of quantitation. The applications have mostly been in the analysis of small molecules and proteins, but its utility is expanding. Protein glycosylation was recently identified as a new paradigm in biomarker discovery for health and disease. A number of recent studies have now identified differential glycosylation patterns associated with health and disease states, including aging, pregnancy, rheumatoid arthritis and different types of cancer. While the use of MRM in clinical glycomics is still in its infancy, it can likely play a role in the quantitation of protein glycosylation in the clinical setting. Here, we aim to review the current advances in the nascent application of MRM in the field of glycomics.

Enhancing MS(n) mass spectrometry strategy for carbohydrate analysis: A b2 ion spectral library.

Glycosylation is an important class of the "postranslationome", which includes manifold aspects of post-translational protein modification, affecting protein conformation, providing ligands for protein receptors [1-5], and encoding unique haptenic [6,7] or antigenic markers for oncology [8-11] and other applications. Identification of individual monomeric units, linkages, ring size, branching and anomerity has posed significant challenges to mass spectrometrists. MS(n) is a growing key instrumental method to differentiate among isomers [12]. While the potential isomers in oligosaccharides are impossibly large [12], likely possibilities can be limited by the biological system, including the expressed glycosyl transferases [13-20]. Mass spectra from sequential stages of collision activation (MS(n)) can supply structural details for precise characterization of linkage, monomer ID, substitutions, anomerity and branching [21-25]. There is a fundamental need for high throughput tools in glycomics to complement proteome studies. In that regard, nothing could be more important than searchable spectral library files for structural confirmation. The National Academy of Science (NAS) report (http://glyco.nas.edu) recommends the need of more than 10,000 synthetic structures of carbohydrates to advance the field of glycomics. This study demonstrates that the general reproducibility of ion trap spectra, and energy independence from modes of ionization and collisional activation, make compiling an MS(n) library for carbohydrate identification an achievable research target [26]. We intend to use the new B2 library for carbohydrate differences found on cancers, where we profile the glycosyltransferases to predict classes of potential structures, and use the library for MS identification of the expected cohort of altered structures.

Individuality Normalization when Labeling with Isotopic Glycan Hydrazide Tags (INLIGHT): A Novel Glycan-Relative Quantification Strategy

The Individuality Normalization when Labeling with Isotopic Glycan Hydrazide Tags (INLIGHT) strategy for the sample preparation, data analysis, and relative quantification of N-linked glycans is presented. Glycans are derivatized with either natural (L) or stable-isotope labeled (H) hydrazide reagents and analyzed using reversed phase liquid chromatography coupled online to a Q Exactive mass spectrometer. A simple glycan ladder, maltodextrin, is first used to demonstrate the relative quantification strategy in samples with negligible analytical and biological variability. It is shown that after a molecular weight correction attributable to isotopic overlap and a post-acquisition normalization of the data to account for any systematic bias, a plot of the experimental H:L ratio versus the calculated H:L ratio exhibits a correlation of unity for maltodextrin samples mixed in different ratios. We also demonstrate that the INLIGHT approach can quantify species over four orders of magnitude in ion abundance. The INLIGHT strategy is further demonstrated in pooled human plasma, where it is shown that the post-acquisition normalization is more effective than using a single spiked-in internal standard. Finally, changes in glycosylation are able to be detected in complex biological matrices, when spiked with a glycoprotein. The ability to spike in a glycoprotein and detect change at the glycan level validates both the sample preparation and data analysis strategy, making INLIGHT an invaluable relative quantification strategy for the field of glycomics.

Structural Glycomic Analyses at High Sensitivity: A Decade of Progress

The field of glycomics has recently advanced in response to the urgent need for structural characterization and quantification of complex carbohydrates in biologically and medically important applications. The recent success of analytical glycobiology at high sensitivity reflects numerous advances in biomolecular mass spectrometry and its instrumentation, capillary and microchip separation techniques, and microchemical manipulations of carbohydrate reactivity. The multimethodological approach appears to be necessary to gain an in-depth understanding of very complex glycomes in different biological systems.

GlycReSoft: A Software Package for Automated Recognition of Glycans from LC/MS Data

Glycosylation modifies the physicochemical properties and protein binding functions of glycoconjugates. These modifications are biosynthesized in the endoplasmic reticulum and Golgi apparatus by a series of enzymatic transformations that are under complex control. As a result, mature glycans on a given site are heterogeneous mixtures of glycoforms. This gives rise to a spectrum of adhesive properties that strongly influences interactions with binding partners and resultant biological effects. In order to understand the roles glycosylation plays in normal and disease processes, efficient structural analysis tools are necessary. In the field of glycomics, liquid chromatography/mass spectrometry (LC/MS) is used to profile the glycans present in a given sample. This technology enables comparison of glycan compositions and abundances among different biological samples, i.e. normal versus disease, normal versus mutant, etc. Manual analysis of the glycan profiling LC/MS data is extremely time-consuming and efficient software tools are needed to eliminate this bottleneck. In this work, we have developed a tool to computationally model LC/MS data to enable efficient profiling of glycans. Using LC/MS data deconvoluted by Decon2LS/DeconTools, we built a list of unique neutral masses corresponding to candidate glycan compositions summarized over their various charge states, adducts and range of elution times. Our work aims to provide confident identification of true compounds in complex data sets that are not amenable to manual interpretation. This capability is an essential part of glycomics work flows. We demonstrate this tool, GlycReSoft, using an LC/MS dataset on tissue derived heparan sulfate oligosaccharides. The software, code and a test data set are publically archived under an open source license.

Analytical glycobiology at high sensitivity: current approaches and directions.

This review summarizes the analytical advances made during the last several years in the structural and quantitative determinations of glycoproteins in complex biological mixtures. The main analytical techniques used in the fields of glycomics and glycoproteomics involve different modes of mass spectrometry and their combinations with capillary separation methods such as microcolumn liquid chromatography and capillary electrophoresis. The need for high-sensitivity measurements have been emphasized in the oligosaccharide profiling used in the field of biomarker discovery through MALDI mass spectrometry. High-sensitivity profiling of both glycans and glycopeptides from biological fluids and tissue extracts has been aided significantly through lectin preconcentration and the uses of affinity chromatography.

Glycoscience aids in biomarker discovery

The glycome consists of all glycans (or carbohydrates) within a biological system, and modulates a wide range of important biological activities, from protein folding to cellular communications. The mining of the glycome for disease markers represents a new paradigm for biomarker discovery; however, this effort is severely complicated by the vast complexity and structural diversity of glycans. This review summarizes recent developments in analytical technology and methodology as applied to the fields of glycomics and glycoproteomics. Mass spectrometric strategies for glycan compositional profiling are described, as are potential refinements which allow structure-specific profiling. Analytical methods that can discern protein glycosylation at a specific site of modification are also discussed in detail. Biomarker discovery applications are shown at each level of analysis, highlighting the key role that glycoscience can play in helping scientists understand disease biology.

Novel data analysis tool for semiquantitative LC-MS-MS2 profiling of N-glycans

Despite recent technical advances in glycan analysis, the rapidly growing field of glycomics still lacks methods that are high throughput and robust, and yet allow detailed and reliable identification of different glycans. LC-MS-MS(2) methods have a large potential for glycan analysis as they enable separation and identification of different glycans, including structural isomers. The major drawback is the complexity of the data with different charge states and adduct combinations. In practice, manual data analysis, still largely used for MALDI-TOF data, is no more achievable for LC-MS-MS(2) data. To solve the problem, we developed a glycan analysis software GlycanID for the analysis of LC-MS-MS(2) data to identify and profile glycan compositions in combination with existing proteomic software. IgG was used as an example of an individual glycoprotein and extracted cell surface proteins of human fibroblasts as a more complex sample to demonstrate the power of the novel data analysis approach. N-glycans were isolated from the samples and analyzed as permethylated sugar alditols by LC-MS-MS(2), permitting semiquantitative glycan profiling. The data analysis consisted of five steps: 1) extraction of LC-MS features and MS(2) spectra, 2) mapping potential glycans based on feature distribution, 3) matching the feature masses with a glycan composition database and de novo generated compositions, 4) scoring MS(2) spectra with theoretical glycan fragments, and 5) composing the glycan profile for the identified glycan compositions. The resulting N-glycan profile of IgG revealed 28 glycan compositions and was in good correlation with the published IgG profile. More than 50 glycan compositions were reliably identified from the cell surface N-glycan profile of human fibroblasts. Use of the GlycanID software made relatively rapid analysis of complex glycan LC-MS-MS(2) data feasible. The results demonstrate that the complexity of glycan LC-MS-MS(2) data can be used as an asset to increase the reliability of the identifications.

A general approach for the purification and quantitative glycomic analysis of human plasma

The development of a general method for the purification and quantitative glycomic analysis of human plasma samples to characterize global glycosylation changes shall be presented. The method involves multiple steps, including the depletion of plasma via multi-affinity chromatography to remove high abundant proteins, the enrichment of the lower abundant glycoproteins via multi-lectin affinity chromatography, the isotopic derivatization of released glycans, and quantitative analysis by MALDI-TOF MS. Isotopic derivatization of glycans is accomplished using the well-established chemistry of reductive amination to derivatize glycans with either a light analog ((12)C anthranilic acid) or a heavy analog ((13)C(7) anthranilic acid), which allows for the direct comparison of the alternately labeled glycans by MALDI-TOF MS. The method displays a tenfold linear dynamic range for both neutral and sialylated glycans with sub-picomolar sensitivity. Additionally, by using anthranilic acid, a very sensitive fluorophore, as the derivatization reagent, the glycans can be analyzed by chromatography with fluorescence detection. The utility of this methodology is highlighted by the many diseases and disorders that are known to either show or be the result of changes in glycosylation. A method that provides a generic approach for sample preparation and quantitative data will help to further advance the field of glycomics.

Understanding Glycomechanics Using Mathematical Modeling: A Review of Current Approaches to Simulate Cellular Glycosylation Reaction Networks

Following the footsteps of genomics and proteomics, recent years have witnessed the growth of large-scale experimental methods in the field of glycomics. In parallel, there has also been growing interest in developing Systems Biology based methods to study the glycome. The combined goals of these endeavors is to identify glycosylation-dependent mechanisms regulating human physiology, check points that can control the progression of pathophysiology, and modifications to reaction pathways that can result in more uniform biopharmaceutical processes. In these efforts, mathematical models of N- and O-linked glycosylation have emerged as paradigms for the field. While these are relatively few in number, nevertheless, the existing models provide a basic framework that can be used to develop more sophisticated analysis strategies for glycosylation in the future. The current review surveys these computational models with focus on the underlying mathematics and assumptions, and with respect to their ability to generate experimentally testable hypotheses.

New Technologies for Glycomic Analysis: Toward a Systematic Understanding of the Glycome

Carbohydrates are the most difficult class of biological molecules to study by high-throughput methods owing to the chemical similarities between the constituent monosaccharide building blocks, template-less biosynthesis, and the lack of clearly identifiable consensus sequences for the glycan modification of cohorts of glycoproteins. These molecules are crucial for a wide variety of cellular processes ranging from cell-cell communication to immunity, and they are altered in disease states such as cancer and inflammation. Thus, there has been a dedicated effort to develop glycan analysis into a high-throughput analytical field termed glycomics. Herein we highlight major advances in applying separation, mass spectrometry, and microarray methods to the fields of glycomics and glycoproteomics. These new analytical techniques are rapidly advancing our understanding of the importance of glycosylation in biology and disease.

Glycomics‐based analysis of chicken red blood cells provides insight into the selectivity of the viral agglutination assay

Agglutination of red blood cells (RBCs), including chicken RBCs (cRBCs), has been used extensively to estimate viral titer, to screen glycan-receptor binding preference, and to assess the protective response of vaccines. Although this assay enjoys widespread use, some virus strains do not agglutinate RBCs. To address these underlying issues and to increase the usefulness of cRBCs as tools for studying viruses, such as influenza, we analyzed the cell surface N-glycans of cRBCs. On the basis of the results obtained from complementary analytical strategies, including MS, 1D and 2D-NMR spectroscopy, exoglycosidase digestions, and HPLC profiling, we report the major glycan structures present on cRBCs. By comparing the glycan structures of cBRCs with those of representative human upper respiratory cells, we offer a possible explanation for the fact that certain influenza strains do not agglutinate cRBCs, using specific human-adapted influenza hemagglutinins as examples. Finally, recent understanding of the role of various glycan structures in high affinity binding to influenza hemagglutinins provides context to our findings. These results illustrate that the field of glycomics can provide important information with respect to the experimental systems used to characterize, detect and study viruses.

GlycomicsDB - A Data Integration Platform for Glycans and their Strucutres

Glycomics is a discipline of biology that deals with the structure and function of glycans (or carbohydrates). Analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are having a significant impact on the field of glycomics. However, effective progress in glycomics research requires collaboration between laboratories to share experimental data, structural information of glycans, and simulation results. Herein we report the development of a web-based data management system that can incorporate large volumes of data from disparate sources and organize them into a uniform format for users to store and access. This system enables participating laboratories to set up a shared data repository which members of interdisciplinary teams can access. The system is able to manage and share raw MS data and structural information of glycans.The database is available at http://www.glycomics.bcf.ku.edu

Discovering new clinical markers in the field of glycomics

Glycosylation modifications have been reported in a number of disease states and, as a result, there is significant focus on the discovery and development of glycan-based biomarkers. Glyco-biomarkers have the potential to enhance the efficacy and efficiency of the diagnostic procedures for these diseases.

The RINGS Resource for Glycome Informatics Analysis and Data Mining on the Web

In the bioinformatics field, many computer algorithmic and data mining technologies have been developed for gene prediction, protein-protein interaction analysis, sequence analysis, and protein folding predictions, to name a few. This kind of research has branched off from the genomics field, creating the transcriptomics, proteomics, metabolomics, and glycomics research areas in the postgenomic age. In the glycomics field, given the complexity of glycan structures with their branches of monosaccharides in various conformations, new data mining and algorithmic methods have been developed in an attempt to gain a better understanding of glycans. However, these methods have not all been implemented as tools such that the glycobiology community may utilize them in their research. Thus, we have developed RINGS (Resource for INformatics of Glycomes at Soka) as a freely available Web resource for glycobiologists to analyze their data using the latest data mining and algorithmic techniques. It provides a number of tools including a 2D glycan drawing and querying interface called DrawRINGS, a Glycan Pathway Predictor (GPP) tool for dynamically computing the N-glycan biosynthesis pathway from a given glycan structure, and data mining tools Glycan Miner Tool and Profile PSTMM. These tools and other utilities provided by RINGS will be described. The URL for RINGS is http://rings.t.soka.ac.jp/.

Imaging Analysis of Carbohydrate-Modified Surfaces Using ToF-SIMS and SPRi

Covalent modification of surfaces with carbohydrates (glycans) is a prerequisite for a variety of glycomics-based biomedical applications, including functional biomaterials, glycoarrays, and glycan-based biosensors. The chemistry of glycan immobilization plays an essential role in the bioavailability and function of the surface bound carbohydrate moiety. However, the scarcity of analytical methods to characterize carbohydrate-modified surfaces complicates efforts to optimize glycan surface chemistries for specific applications. Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a surface sensitive technique suited for probing molecular composition at the biomaterial interface. Expanding ToF-SIMS analysis to interrogate carbohydrate-modified materials would increase our understanding of glycan surface chemistries and advance novel tools in the nascent field of glycomics. In this study, a printed glycan microarray surface was fabricated and subsequently characterized by ToF-SIMS imaging analysis. A multivariate technique based on principal component analysis (PCA) was used to analyze the ToF-SIMS dataset and reconstruct ToF-SIMS images of functionalized surfaces. These images reveal chemical species related to the immobilized glycan, underlying glycan-reactive chemistries, gold substrates, and outside contaminants. Printed glycoarray elements (spots) were also interrogated to resolve the spatial distribution and spot homogeneity of immobilized glycan. The bioavailability of the surface-bound glycan was validated using a specific carbohydrate-binding protein (lectin) as characterized by Surface Plasmon Resonance Imaging (SPRi). Our results demonstrate that ToF-SIMS is capable of characterizing chemical features of carbohydrate-modified surfaces and, when complemented with SPRi, can play an enabling role in optimizing glycan microarray fabrication and performance.