Global comparisons of lectin-glycan interactions using a database of analyzed glycan array data

Boltzmann Model Predicts Glycan Structures from Lectin Binding

Glycans are complex oligosaccharides that are involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese hamster ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

A Boltzmann model predicts glycan structures from lectin binding

Glycans are complex oligosaccharides involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 ± 7% of N-glycans and 87 ± 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese Hamster Ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities

Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving; however, the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well-defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use a combination of machine learning algorithms and expert annotation to define lectin specificity for this important probe set. Our analysis uses comprehensive glycan microarray analysis of commercially available lectins we obtained using version 5.0 of the Consortium for Functional Glycomics glycan microarray (CFGv5). This data set was made public in 2011. We report the creation of this data set and its use in large-scale evaluation of lectin-glycan binding behaviors. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides and linkages. Combining machine learning with manual annotation, we create a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents.

Development and applications of sialoglycan-recognizing probes (SGRPs) with defined specificities: exploring the dynamic mammalian sialoglycome

Glycans that are abundantly displayed on vertebrate cell surface and secreted molecules are often capped with terminal sialic acids (Sias). These diverse 9-carbon-backbone monosaccharides are involved in numerous intrinsic biological processes. They also interact with commensals and pathogens, while undergoing dynamic changes in time and space, often influenced by environmental conditions. However, most of this sialoglycan complexity and variation remains poorly characterized by conventional techniques, which often tend to destroy or overlook crucial aspects of Sia diversity and/or fail to elucidate native structures in biological systems, i.e. in the intact sialome. To date, in situ detection and analysis of sialoglycans has largely relied on the use of plant lectins, sialidases, or antibodies, whose preferences (with certain exceptions) are limited and/or uncertain. We took advantage of naturally evolved microbial molecules (bacterial adhesins, toxin subunits, and viral hemagglutinin-esterases) that recognize sialoglycans with defined specificity to delineate 9 classes of sialoglycan recognizing probes (SGRPs: SGRP1-SGRP9) that can be used to explore mammalian sialome changes in a simple and systematic manner, using techniques common in most laboratories. SGRP candidates with specificity defined by sialoglycan microarray studies were engineered as tagged probes, each with a corresponding nonbinding mutant probe as a simple and reliable negative control. The optimized panel of SGRPs can be used in methods commonly available in most bioscience labs, such as ELISA, western blot, flow cytometry, and histochemistry. To demonstrate the utility of this approach, we provide examples of sialoglycome differences in tissues from C57BL/6 wild-type mice and human-like Cmah-/- mice.

LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction

Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host-microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

Effects of Dietary Doum Palm Fruit Powder on Growth, Antioxidant Capacity, Immune Response, and Disease Resistance of African Catfish, Clarias gariepinus (B.)

Application of herbal immune-stimulants for modulation of fish growth and immune response has received great interest during the past decades. With several pharmacological properties, Doum palm, Hyphaene thebaica (Mart.) is known to be a beneficial medicinal plant. The objective of this study was to investigate the effects of the dietary addition of doum palm fruit powder (DPFP) on growth performance, non-specific immune response, and antioxidant parameters of African catfish, Clarias gariepinus (B.). A total of 120 fish (average initial weight 60.50 ± 0.04 g) were randomly allocated to four groups (three replicates/group, 10 fish/aquarium); a basal diet without DPFP supplementation was used as a control, and three other diets were prepared by supplementing 5, 10, or 15 g kg^-1 DPFP for a ten-week feeding period. Following ten weeks of feeding, the fish were challenged with Aeromonas hydrophila (as an immune challenge test), and mortalities were recorded. In comparison to the control diet, dietary DPFP significantly improved growth parameters, including final body weight, body weight gain (WG), specific growth rate (SGR), feed conversion ratio (FCR), and protein efficiency ratio (PER), along with an increase in the content of dry matter of the whole body, in a concentration-dependent manner. Moreover, the heights of intestinal villi, numbers of goblet cells, and intraepithelial lymphocytes (IEL) exhibited marked escalation in all parts of the intestine by increasing the level of DPFP, except for numbers of IEL in the proximal part. The decline in serum glucose, cholesterol, and triglyceride levels was prominent in DPFP10 and DPFP15 groups respective to the DPFP0 group. Furthermore, DPFP boosted the hepatic level of catalase (CAT) in the fish, in a dose-dependent manner; meanwhile, the activity of superoxide dismutase (SOD) and reduced glutathione (GSH) content were also augmented in DPFP10 and DPFP15 groups respective to the DPFP0 group. Dietary DPFP (DPFP15 followed by DPFP10 then DPFP5) led to a pronounced enhancement in the innate immune response (phagocytic percent and index, lysozyme activity, nitric oxide (NO) production, and sialoglycans, namely α 2,3-sialyltransferase and α 2,6-sialyltransferase content); however, the myeloperoxidase (MPO) activity was reduced. Significantly higher relative percentage survival (RPS, 88.56%) of the fish, following the A. hydrophila challenge, was observed for the DPFP15 group. We can suggest that DPFP can beneficially influence fish growth, intestinal histomorphology, hepatic levels of catalase (CAT), superoxide dismutase (SOD) activity and glutathione (GSH) content, immune response, and disease resistance against A. hydrophila challenge.

Microarray analyses of closely related glycoforms reveal different accessibilities of glycan determinants on N-glycan branches

Glycans mediate a wide variety of biological roles via recognition by glycan-binding proteins (GBPs). Comprehensive knowledge of such interaction is thus fundamental to glycobiology. While the primary binding feature of GBPs can be easily uncovered by using a simple glycan microarray harboring limited numbers of glycan motifs, their fine specificities are harder to interpret. In this study, we prepared 98 closely related N-glycoforms that contain 5 common glycan epitopes which allowed the determination of the fine binding specificities of several plant lectins and anti-glycan antibodies. These N-glycoforms differ from each other at the monosaccharide level and were presented in an identical format to ensure comparability. With the analysis platform we used, it was found that most tested GBPs have preferences toward only one branch of the complex N-glycans, and their binding toward the epitope-presenting branch can be significantly affected by structures on the other branch. Fine specificities described here are valuable for a comprehensive understanding and applications of GBPs.

Identifying glycan motifs using a novel subtree mining approach

Background

Glycans are complex sugar chains, crucial to many biological processes. By participating in binding interactions with proteins, glycans often play key roles in host–pathogen interactions. The specificities of glycan-binding proteins, such as lectins and antibodies, are governed by motifs within larger glycan structures, and improved characterisations of these determinants would aid research into human diseases. Identification of motifs has previously been approached as a frequent subtree mining problem, and we extend these approaches with a glycan notation that allows recognition of terminal motifs.

Results

In this work, we customised a frequent subtree mining approach by altering the glycan notation to include information on terminal connections. This allows specific identification of terminal residues as potential motifs, better capturing the complexity of glycan-binding interactions. We achieved this by including additional nodes in a graph representation of the glycan structure to indicate the presence or absence of a linkage at particular backbone carbon positions. Combining this frequent subtree mining approach with a state-of-the-art feature selection algorithm termed minimum-redundancy, maximum-relevance (mRMR), we have generated a classification pipeline that is trained on data from a glycan microarray. When applied to a set of commonly used lectins, the identified motifs were consistent with known binding determinants. Furthermore, logistic regression classifiers trained using these motifs performed well across most lectins examined, with a median AUC value of 0.89.

Conclusions

We present here a new subtree mining approach for the classification of glycan binding and identification of potential binding motifs. The Carbohydrate Classification Accounting for Restricted Linkages (CCARL) method will assist in the interpretation of glycan microarray experiments and will aid in the discovery of novel binding motifs for further experimental characterisation.

Advances in Tools to Determine the Glycan-Binding Specificities of Lectins and Antibodies

Proteins that bind carbohydrate structures can serve as tools to quantify or localize specific glycans in biological specimens. Such proteins, including lectins and glycan-binding antibodies, are particularly valuable if accurate information is available about the glycans that a protein binds. Glycan arrays have been transformational for uncovering rich information about the nuances and complexities of glycan-binding specificity. A challenge, however, has been the analysis of the data. Because protein-glycan interactions are so complex, simplistic modes of analyzing the data and describing glycan-binding specificities have proven inadequate in many cases. This review surveys the methods for handling high-content data on protein-glycan interactions. We contrast the approaches that have been demonstrated and provide an overview of the resources that are available. We also give an outlook on the promising experimental technologies for generating new insights into protein-glycan interactions, as well as a perspective on the limitations that currently face the field.

The Repertoire of Human Antiglycan Antibodies and Its Dynamics in the First Year of Life

The repertoire of antiglycan antibodies of peripheral blood was studied using a microarray containing 487 glycan antigens: fragments of mammalian glycans (N- and O-chains of glycoproteins, as well as glycolipids) and also bacterial polysaccharides. The sera samples correspond to the third, sixth, and twelfth months of life. The infants were divided into four groups according to their nutrition type: breast milk, standard formula, and partially or extensively hydrolyzed formula. During the first year of life, the total amount of IgG decreased; presumably, the lifetime of maternal IgG in the newborns' bloodstream is much greater than is generally assumed. At the same time, the IgM content was low during the first six months and increased significantly by the twelfth month. The antiglycan IgM repertoire of one-year-old infants was still different from that of their mothers, as well as from the repertoire of unrelated donors, in particular, by the absence of antibodies against the Galβ1-3GlcNAc (Le^C) disaccharide, which is found in almost all healthy humans. It is noteworthy that the level of IgM of breast-fed infants was significantly lower than that of formula-fed by the twelfth month.

Current Concepts on 6-sulfo LacNAc Expressing Monocytes (slanMo)

The human mononuclear phagocytes system consists of dendritic cells (DCs), monocytes, and macrophages having different functions in bridging innate and adaptive immunity. Among the heterogeneous population of monocytes the cell surface marker slan (6-sulfo LacNAc) identifies a specific subset of human CD14^- CD16⁺ non-classical monocytes, called slan⁺ monocytes (slanMo). In this review we discuss the identity and functions of slanMo, their contributions to immune surveillance by pro-inflammatory cytokine production, and cross talk with T cells and NK cells. We also consider the role of slanMo in the regulation of chronic inflammatory diseases and cancer. Finally, we highlight unresolved questions that should be the focus of future research.

Metabolomic and glycomic findings in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is a stressor-related disorder that develops in a subset of individuals exposed to a traumatic experience. Factors associated with vulnerability to PTSD are still not fully understood. PTSD is frequently comorbid with various psychiatric and somatic disorders, moderate response to treatment and remission rates. The term "theranostics" combines diagnosis, prognosis, and therapy and offers targeted therapy based on specific analyses. Theranostics, combined with novel techniques and approaches called "omics", which integrate genomics, transcriptomic, proteomics and metabolomics, might improve knowledge about biological underpinning of PTSD, and offer novel therapeutic strategies. The focus of this review is on metabolomic and glycomic data in PTSD. Metabolomics evaluates changes in the metabolome of an organism by exploring the set of small molecules (metabolites), while glycomics studies the glycome, a complete repertoire of glycan structures with their functional roles in biological systems. Both metabolome and glycome reflect the physiological and pathological conditions in individuals. Only a few studies evaluated metabolic and glycomic changes in patients with PTSD. The metabolomics studies in PTSD patients uncovered different metabolites that might be associated with psychopathological alterations in PTSD. The glycomics study in PTSD patients determined nine N-glycan structures and found accelerated and premature aging in traumatized subjects and subjects with PTSD based on a GlycoAge index. Therefore, further larger studies and replications are needed. Better understanding of the biological basis of PTSD, including metabolomic and glycomic data, and their integration with other "omics" approaches, might identify new molecular targets and might provide improved therapeutic approaches.

Distinct substrate specificities of human GlcNAc-6-sulfotransferases revealed by mass spectrometry-based sulfoglycomic analysis

Sulfated glycans are known to be involved in several glycan-mediated cell adhesion and recognition pathways. Our mRNA transcript analyses on the genes involved in synthesizing GlcNAc-6-O-sulfated glycans in human colon cancer tissues indicated that GlcNAc6ST-2 (CHST4) is preferentially expressed in cancer cells compared with nonmalignant epithelial cells among the three known major GlcNAc-6-O-sulfotransferases. On the contrary, GlcNAc6ST-3 (CHST5) was only expressed in nonmalignant epithelial cells, whereas GlcNAc6ST-1 (CHST2) was expressed equally in both cancerous and nonmalignant epithelial cells. These results suggest that 6-O-sulfated glycans that are synthesized only by GlcNAc6ST-2 may be highly colon cancer-specific, as supported by immunohistochemical staining of cancer cells using the MECA-79 antibody known to be relatively specific to the enzymatic reaction products of GlcNAc6ST-2. By more precise MS-based sulfoglycomic analyses, we sought to further infer the substrate specificities of GlcNAc6STs via a definitive mapping of various sulfo-glycotopes and O-glycan structures expressed in response to overexpression of transfected GlcNAc6STs in the SW480 colon cancer cell line. By detailed MS/MS sequencing, GlcNAc6ST-3 was shown to preferentially add sulfate onto core 2-based O-glycan structures, but it does not act on extended core 1 structures, whereas GlcNAc6ST-1 prefers core 2-based O-glycans to extended core 1 structures. In contrast, GlcNAc6ST-2 could efficiently add sulfate onto both extended core 1- and core 2-based O-glycans, leading to the production of unique sulfated extended core 1 structures such as R-GlcNAc(6-SO3-)β1-3Galβ1-4GlcNAc(6-SO3-)β1-3Galβ1-3GalNAcα, which are good candidates to be targeted as cancer-specific glycans.

Predicting the Structures of Glycans, Glycoproteins, and Their Complexes

Complex carbohydrates are ubiquitous in nature, and together with proteins and nucleic acids they comprise the building blocks of life. But unlike proteins and nucleic acids, carbohydrates form nonlinear polymers, and they are not characterized by robust secondary or tertiary structures but rather by distributions of well-defined conformational states. Their molecular flexibility means that oligosaccharides are often refractory to crystallization, and nuclear magnetic resonance (NMR) spectroscopy augmented by molecular dynamics (MD) simulation is the leading method for their characterization in solution. The biological importance of carbohydrate-protein interactions, in organismal development as well as in disease, places urgency on the creation of innovative experimental and theoretical methods that can predict the specificity of such interactions and quantify their strengths. Additionally, the emerging realization that protein glycosylation impacts protein function and immunogenicity places the ability to define the mechanisms by which glycosylation impacts these features at the forefront of carbohydrate modeling. This review will discuss the relevant theoretical approaches to studying the three-dimensional structures of this fascinating class of molecules and interactions, with reference to the relevant experimental data and techniques that are key for validation of the theoretical predictions.

A potential impact of Helicobacter pylori-related galectin-3 in neurodegeneration

Neurodegeneration represents a component of the central nervous system (CNS) diseases pathogenesis, either as a disability primary source in the frame of prototype neurodegenerative disorders, or as a secondary effect, following inflammation, hypoxia or neurotoxicity. Galectins are members of the lectin superfamily, a group of endogenous glycan-binding proteins, able to interact with glycosylated receptors expressed by several immune cell types. Glycan-lectin interactions play critical roles in the living systems by involving and mediating a variety of biologically important normal and pathological processes, including cell-cell signaling shaping cell communication, proliferation and migration, immune responses and fertilization, host-pathogen interactions and diseases such as neurodegenerative disorders and tumors. This review focuses in the role of Galectin-3 in shaping responses of the immune system against microbial agents, and concretely, Helicobacter pylori (Hp), thereby potentiating effect of the microbe in areas distant from the ordinary site of colonization, like the CNS. We hereby postulate that gastrointestinal Hp alterations in terms of immune cell functional phenotype, cytokine and chemokine secretion, may trigger systemic responses, thereby conferring implications for remote processes susceptible in immunity disequilibrium, namely, the CNS inflammation and/or neurodegeneration.

Lectins identify distinct populations of coelomocytes in Strongylocentrotus purpuratus

Coelomocytes represent the immune cells of echinoderms, but detailed knowledge about their roles during immune responses is very limited. One major challenge for studying coelomocyte biology is the lack of reagents to identify and purify distinct populations defined by objective molecular markers rather than by morphology-based classifications that are subjective at times. Glycosylation patterns are known to differ significantly between cell types in vertebrates, and furthermore they can vary depending on the developmental stage and activation states within a given lineage. Thus fluorescently labeled lectins that recognize distinct glycan structures on cell surface proteins are routinely used to identify discrete cell populations in the vertebrate immune system. Here we now employed a panel of fifteen fluorescently-labeled lectins to determine differences in the glycosylation features on the surface of Strongylocentrotus purpuratus coelomocytes by fluorescence microscopy and flow cytometry. Eight of the lectins (succinylated wheat germ agglutinin, Len culinaris lectin, Pisum sativum agglutinin, Saphora japonica agglutinin, Solanum tuberosum lectin, Lycopersicon esculentum lectin, Datura stramonium lectin, Vicia villosa lectin) showed distinct binding patterns to fixed and live cells of three major coelomocyte classes: phagocytic cells, red spherule cells, and vibratile cells. Importantly, almost all lectins bound only to a subgroup of cells within each cell type. Lastly, we established fluorescently-labeled lectin-based fluorescence activated cell sorting as a strategy to purify distinct S. purpuratus coelomocyte (sub-)populations based on molecular markers. We anticipate that this will become a routine approach in future studies focused on dissecting the roles of different coelomocytes in echinoderm immunity.

Mining High-Complexity Motifs in Glycans: A New Language To Uncover the Fine Specificities of Lectins and Glycosidases

Knowledge of lectin and glycosidase specificities is fundamental to the study of glycobiology. The primary specificities of such molecules can be uncovered using well-established tools, but the complex details of their specificities are difficult to determine and describe. Here we present a language and algorithm for the analysis and description of glycan motifs with high complexity. The language uses human-readable notation and wildcards, modifiers, and logical operators to define motifs of nearly any complexity. By applying the syntax to the analysis of glycan-array data, we found that the lectin AAL had higher binding where fucose groups are displayed on separate branches. The lectin SNA showed gradations in binding based on the length of the extension displaying sialic acid and on characteristics of the opposing branches. A new algorithm to evaluate changes in lectin binding upon treatment with exoglycosidases identified the primary specificities and potential fine specificities of an α1–2-fucosidase and an α2–3,6,8-neuraminidase. The fucosidase had significantly lower action where sialic acid neighbors the fucose, and the neuraminidase showed statistically lower action where α1–2 fucose neighbors the sialic acid or is on the opposing branch. The complex features identified here would have been inaccessible to analysis using previous methods. The new language and algorithms promise to facilitate the precise determination and description of lectin and glycosidase specificities.

A Novel Molecular Model of Plant Lectin-Induced Programmed Cell Death in Cancer

Plant lectin, a class of highly diverse non-immune origin and carbohydrate-binding proteins, has been reported to specially induce cancer cell through programmed cell death (PCD) pathways (apoptosis and/or autophagy), shedding lights on screening promising anti-cancer candidate agent for further therapeutic trials. However, the complicated molecular mechanisms by which plant lectins induced the programmed death of tumor cells, have not yet been fully clarified. Here, we summarized a novel model, based on vast amount of research, by which plant lectins eliminate various types of cancer cells via three major pathways, including a) direct ribosome inactivating, b) endocytosis-dependent mitochondrial dysfunction and c) sugar-containing receptors binding. A better understanding of the role of plant lectins played and further elucidation of the strategies targeting PCD would provide a new clue for the applications and modifications of plant lectin as a potential anti-cancer agent from bench to clinic.

Characterizing Protein Glycosylation through On-Chip Glycan Modification and Probing

Glycans are critical to protein biology and are useful as disease biomarkers. Many studies of glycans rely on clinical specimens, but the low amount of sample available for some specimens limits the experimental options. Here we present a method to obtain information about protein glycosylation using a minimal amount of protein. We treat proteins that were captured or directly spotted in small microarrays (2.2 mm × 2.2 mm) with exoglycosidases to successively expose underlying features, and then we probe the native or exposed features using a panel of lectins or glycan-binding reagents. We developed an algorithm to interpret the data and provide predictions about the glycan motifs that are present in the sample. We demonstrated the efficacy of the method to characterize differences between glycoproteins in their sialic acid linkages and N-linked glycan branching, and we validated the assignments by comparing results from mass spectrometry and chromatography. The amount of protein used on-chip was about 11 ng. The method also proved effective for analyzing the glycosylation of a cancer biomarker in human plasma, MUC5AC, using only 20 μL of the plasma. A glycan on MUC5AC that is associated with cancer had mostly 2,3-linked sialic acid, whereas other glycans on MUC5AC had a 2,6 linkage of sialic acid. The on-chip glycan modification and probing (on-chip GMAP) method provides a platform for analyzing protein glycosylation in clinical specimens and could complement the existing toolkit for studying glycosylation in disease.

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

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

Structural basis for sulfation-dependent self-glycan recognition by the human immune-inhibitory receptor Siglec-8

Siglec-8 is a human immune-inhibitory receptor that, when engaged by specific self-glycans, triggers eosinophil apoptosis and inhibits mast cell degranulation, providing an endogenous mechanism to down-regulate immune responses of these central inflammatory effector cells. Here we used solution NMR spectroscopy to dissect the fine specificity of Siglec-8 toward different sialylated and sulfated carbohydrate ligands and determined the structure of the Siglec-8 lectin domain in complex with its prime glycan target 6'-sulfo sialyl Lewis(x) A canonical motif for sialic acid recognition, extended by a secondary motif formed by unique loop regions, recognizing 6-O-sulfated galactose dictates tight specificity distinct from other Siglec family members and any other endogenous glycan recognition receptors. Structure-guided mutagenesis revealed key contacts of both interfaces to be equally essential for binding. Our work provides critical structural and mechanistic insights into how Siglec-8 selectively recognizes its glycan target, rationalizes the functional impact of site-specific glycan sulfation in modulating this lectin-glycan interaction, and will enable the rational design of Siglec-8-targeted agonists to treat eosinophil- and mast cell-related allergic and inflammatory diseases, such as asthma.

Glycomic Approaches for the Discovery of Targets in Gastrointestinal Cancer

Gastrointestinal (GI) cancer is the most common group of malignancies and many of its types are among the most deadly. Various glycoconjugates have been used in clinical practice as serum biomarker for several GI tumors, however, with limited diagnose application. Despite the good accessibility by endoscopy of many GI organs, the lack of reliable serum biomarkers often leads to late diagnosis of malignancy and consequently low 5-year survival rates. Recent advances in analytical techniques have provided novel glycoproteomic and glycomic data and generated functional information and putative biomarker targets in oncology. Glycosylation alterations have been demonstrated in a series of glycoconjugates (glycoproteins, proteoglycans, and glycosphingolipids) that are involved in cancer cell adhesion, signaling, invasion, and metastasis formation. In this review, we present an overview on the major glycosylation alterations in GI cancer and the current serological biomarkers used in the clinical oncology setting. We further describe recent glycomic studies in GI cancer, namely gastric, colorectal, and pancreatic cancer. Moreover, we discuss the role of glycosylation as a modulator of the function of several key players in cancer cell biology. Finally, we address several state-of-the-art techniques currently applied in this field, such as glycomic and glycoproteomic analyses, the application of glycoengineered cell line models, microarray and proximity ligation assay, and imaging mass spectrometry, and provide an outlook to future perspectives and clinical applications.

A Panel of Recombinant Mucins Carrying a Repertoire of Sialylated O-Glycans Based on Different Core Chains for Studies of Glycan Binding Proteins

Sialylated glycans serve as key elements of receptors for many viruses, bacteria, and bacterial toxins. The microbial recognition and their binding specificity can be affected by the linkage of the terminal sugar residue, types of underlying sugar chains, and the nature of the entire glycoconjugate. Owing to the pathobiological significance of sialylated glycans, we have engineered Chinese hamster ovary (CHO) cells to secrete mucin-type immunoglobulin-fused proteins carrying terminal α2,3- or α2,6-linked sialic acid on defined O-glycan core saccharide chains. Besides stably expressing P-selectin glycoprotein ligand-1/mouse immunoglobulin G2b cDNA (PSGL-1/mIgG2b), CHO cells were stably transfected with plasmids encoding glycosyltransferases to synthesize core 2 (GCNT1), core 3 (B3GNT6), core 4 (GCNT1 and B3GNT6), or extended core 1 (B3GNT3) chains with or without the type 1 chain-encoding enzyme B3GALT5 and ST6GAL1. Western blot and liquid chromatography-mass spectrometry analysis confirmed the presence of core 1, 2, 3, 4, and extended core 1 chains carrying either type 1 (Galb3GlcNAc) or type 2 (Galb4GlcNAc) outer chains with or without α2,6-linked sialic acids. This panel of recombinant mucins carrying a repertoire of sialylated O-glycans will be important tools in studies aiming at determining the fine O-glycan binding specificity of sialic acid-specific microbial adhesins and mammalian lectins.

Mammalian protein glycosylation--structure versus function

Carbohydrates fulfil many common as well as extremely important functions in nature. They show a variety of molecular displays--e.g., free mono-, oligo-, and polysaccharides, glycolipids, proteoglycans, glycoproteins, etc.--with particular roles and localizations in living organisms. Structure-specific peculiarities are so many and diverse that it becomes virtually impossible to cover them all from an analytical perspective. Hence this manuscript, focused on mammalian glycosylation, rather than a complete list of analytical descriptors or recognized functions for carbohydrate structures, comprehensively reviews three central issues in current glycoscience, namely (i) structural analysis of glycoprotein glycans, covering both classical and novel approaches for teasing out the structural puzzle as well as potential pitfalls of these processes; (ii) an overview of functions attributed to carbohydrates, covering from monosaccharide to complex, well-defined epitopes and full glycans, including post-glycosylational modifications, and (iii) recent technical advances allowing structural identification of glycoprotein glycans with simultaneous assignation of biological functions.

Upregulation of glycans containing 3' fucose in a subset of pancreatic cancers uncovered using fusion-tagged lectins

The fucose post-translational modification is frequently increased in pancreatic cancer, thus forming the basis for promising biomarkers, but a subset of pancreatic cancer patients does not elevate the known fucose-containing biomarkers. We hypothesized that such patients elevate glycan motifs with fucose in linkages and contexts different from the known fucose-containing biomarkers. We used a database of glycan array data to identify the lectins CCL2 to detect glycan motifs with fucose in a 3' linkage; CGL2 for motifs with fucose in a 2' linkage; and RSL for fucose in all linkages. We used several practical methods to test the lectins and determine the optimal mode of detection, and we then tested whether the lectins detected glycans in pancreatic cancer patients who did not elevate the sialyl-Lewis A glycan, which is upregulated in ∼75% of pancreatic adenocarcinomas. Patients who did not upregulate sialyl-Lewis A, which contains fucose in a 4' linkage, tended to upregulate fucose in a 3' linkage, as detected by CCL2, but they did not upregulate total fucose or fucose in a 2' linkage. CCL2 binding was high in cancerous epithelia from pancreatic tumors, including areas negative for sialyl-Lewis A and a related motif containing 3' fucose, sialyl-Lewis X. Thus, glycans containing 3' fucose may complement sialyl-Lewis A to contribute to improved detection of pancreatic cancer. Furthermore, the use of panels of recombinant lectins may uncover details about glycosylation that could be important for characterizing and detecting cancer.

Glycan motif profiling reveals plasma sialyl-lewis x elevations in pancreatic cancers that are negative for sialyl-lewis A

The sialyl-Lewis A (sLeA) glycan forms the basis of the CA19-9 assay and is the current best biomarker for pancreatic cancer, but because it is not elevated in ∼25% of pancreatic cancers, it is not useful for early diagnosis. We hypothesized that sLeA-low tumors secrete glycans that are related to sLeA but not detectable by CA19-9 antibodies. We used a method called motif profiling to predict that a structural isomer of sLeA called sialyl-Lewis X (sLeX) is elevated in the plasma of some sLeA-low cancers. We corroborated this prediction in a set of 48 plasma samples and in a blinded set of 200 samples. An antibody sandwich assay formed by the capture and detection of sLeX was elevated in 13 of 69 cancers that were not elevated in sLeA, and a novel hybrid assay of sLeA capture and sLeX detected 24 of 69 sLeA-low cancers. A two-marker panel based on combined sLeA and sLeX detection differentiated 109 pancreatic cancers from 91 benign pancreatic diseases with 79% accuracy (74% sensitivity and 78% specificity), significantly better than sLeA alone, which yielded 68% accuracy (65% sensitivity and 71% specificity). Furthermore, sLeX staining was evident in tumors that do not elevate plasma sLeA, including those with poorly differentiated ductal adenocarcinoma. Thus, glycan-based biomarkers could characterize distinct subgroups of patients. In addition, the combined use of sLeA and sLeX, or related glycans, could lead to a biomarker panel that is useful in the clinical diagnosis of pancreatic cancer. Précis: This paper shows that a structural isomer of the current best biomarker for pancreatic cancer, CA19-9, is elevated in the plasma of patients who are low in CA19-9, potentially enabling more comprehensive detection and classification of pancreatic cancers.

The detection and discovery of glycan motifs in biological samples using lectins and antibodies: new methods and opportunities

Recent research has uncovered unexpected ways that glycans contribute to biology, as well as new strategies for combatting disease using approaches involving glycans. To make full use of glycans for clinical applications, we need more detailed information on the location, nature, and dynamics of glycan expression in vivo. Such studies require the use of specimens acquired directly from patients. Effective studies of clinical specimens require low-volume assays, high precision measurements, and the ability to process many samples. Assays using affinity reagents-lectins and glycan-binding antibodies-can meet these requirements, but further developments are needed to make the methods routine and effective. Recent advances in the use of glycan-binding proteins involve improved determination of specificity using glycan arrays; the availability of databases for mining and analyzing glycan array data; lectin engineering methods; and the ability to quantitatively interpret lectin measurements. Here, we describe many of the challenges and opportunities involved in the application of these new approaches to the study of biological samples. The new tools hold promise for developing methods to improve the outcomes of patients afflicted with diseases characterized by aberrant glycan expression.

Biophysical characterization of lectin–glycan interactions for therapeutics, vaccines and targeted drug-delivery

Lectin–glycan interactions play a role in biological processes, host–pathogen interactions and in disease. A more detailed understanding of these interactions is not only useful for the elucidation of their biological function but can also be applied in immunology, drug development and delivery and diagnostics. We review some commonly used biophysical techniques for studying lectin–glycan interactions; namely: frontal affinity chromatography, glycan/lectin microarray, surface plasmon resonance, electrochemical impedance spectroscopy, isothermal titration calorimetry, fluorescent assays, enzyme linked lectin sorbent assay and saturation transfer difference nuclear magnetic resonance spectroscopy. Each method is evaluated on efficiency, cost and throughput. We also consider the advantages and limitations of each technique and provide examples of their application in biology, drug discovery and delivery, immunology, glycoprofiling and biosensing.

The challenge and promise of glycomics

Glycomics is a broad and emerging scientific discipline focused on defining the structures and functional roles of glycans in biological systems. The staggering complexity of the glycome, minimally defined as the repertoire of glycans expressed in a cell or organism, has resulted in many challenges that must be overcome; these are being addressed by new advances in mass spectrometry as well as by the expansion of genetic and cell biology studies. Conversely, identifying the specific glycan recognition determinants of glycan-binding proteins by employing the new technology of glycan microarrays is providing insights into how glycans function in recognition and signaling within an organism and with microbes and pathogens. The promises of a more complete knowledge of glycomes are immense in that glycan modifications of intracellular and extracellular proteins have critical functions in almost all biological pathways.

Glycan arrays as tools for infectious disease research

Infectious diseases cause millions of deaths worldwide each year and are a major burden for economies, especially in underdeveloped countries. Glycans and their interactions with other biomolecules are involved in all major steps of infection. Glycan arrays enable the rapid and sensitive detection of those interactions and are among the most powerful techniques to study the molecular biology of infectious diseases. This review will focus on recent developments and discuss the applications of glycan arrays to the elucidation of host-pathogen and pathogen-pathogen interactions, the development of tools for infection diagnosis and the use of glycan arrays in modern vaccine design.

Exploration of sialic acid diversity and biology using sialoglycan microarrays

Sialic acids (Sias) are a group of α-keto acids with a nine-carbon backbone, which display many types of modifications in nature. The diversity of natural Sia presentations is magnified by a variety of glycosidic linkages to underlying glycans, the sequences and classes of such glycans, as well as the spatial organization of Sias with their surroundings. This diversity is closely linked to the numerous and varied biological functions of Sias. Relatively large libraries of natural and unnatural Sias have recently been chemically/chemoenzymatically synthesized and/or isolated from natural sources. The resulting sialoglycan microarrays have proved to be valuable tools for the exploration of diversity and biology of Sias. Here we provide an overview of Sia diversity in nature, the approaches used to generate sialoglycan microarrays, and the achievements and challenges arising.

Using antibody arrays to measure protein abundance and glycosylation: considerations for optimal performance

Antibody arrays provide a valuable method for obtaining multiple protein measurements from small volumes of biological samples. Antibody arrays can be designed to target not only core protein abundances (relative or absolute abundances, depending on the availability of standards for calibration), but also posttranslational modifications, provided antibodies or other affinity reagents are available to detect them. Glycosylation is a common modification that has important and diverse functions in both normal and disease biology. Significant progress has been made in developing methods for measuring glycan levels on multiple specific proteins using antibody arrays and glycan-binding reagents. This unit describes practical approaches for developing, optimizing, and using antibody array assays to determine both protein abundance and glycosylation state. Low-volume arrays can be used to reduce sample consumption, and a new way to improve the binding strength of particular glycan-binding reagents through multimerization is discussed. These methods can be useful for a wide range of biological studies in which glycosylation may change and/or affect protein function.

Prediction of glycan motifs using quantitative analysis of multi-lectin binding: Motifs on MUC1 produced by cultured pancreatic cancer cells

PURPOSE

Lectins are valuable tools for detecting specific glycans in biological samples, but the interpretation of the measurements can be ambiguous due to the complexities of lectin specificities. Here, we present an approach to improve the accuracy of interpretation by converting lectin measurements into quantitative predictions of the presence of various glycan motifs.

EXPERIMENTAL DESIGN

The conversion relies on a database of analyzed glycan array data that provides information on the specificities of the lectins for each of the motifs. We tested the method using measurements of lectin binding to glycans on glycan arrays and then applied the method to predicting motifs on the protein mucin 1 (MUC1) expressed in eight different pancreatic cancer cell lines.

RESULTS

The combined measurements from several lectins were more accurate than individual measurements for predicting the presence or absence of motifs on arrayed glycans. The analysis of MUC1 revealed that each cell line expressed a unique pattern of glycoforms, and that the glycoforms significantly differed between MUC1 collected from conditioned media and MUC1 collected from cell lysates.

CONCLUSIONS AND CLINICAL RELEVANCE

This new method could provide more accurate analyses of glycans in biological sample and make the use of lectins more practical and effective for a broad range of researchers.

Coupling Mass Spectrometry-Based "Omic" Sciences with Bioguided Processing to Unravel Milk's Hidden Bioactivities

Many of milk's functional molecules could not be discovered until the right concordance of novel separation and analytical technologies were developed and applied. Many health-promoting components still await discovery due to technical challenges in their identification, isolation and testing. As new analytical technologies are assembled, new functional milk molecules will be discovered. Bovine milk is a source of a wide array of known bioactive compounds from a variety of molecular classes, including free glycans, lipids, glycolipids, peptides, proteins, glycoproteins, stem cells and microRNA. Because milk is such a complex mixture, when analyzed without fractionation or purification, many components mask the analytical signal of others, so some components cannot be detected. Modern analytics allow for the discovery and characterization of hundreds of novel milk compounds with high-resolution and high-accuracy. Liquid chromatography paired with electrospray ionization allows the separation of peptides, glycans and glycolipids for improved mass spectrometric detection. Target proteins and glycoproteins can now be purified from intact milk or other dairy streams by chromatography in order to better characterize these proteins for new bioactivities. The combination of advanced analytics with the new engineering capabilities will allow for high molecular resolution and separation techniques that can be scaled-up to semi-industrial and industrial scale for translation of lab-based discoveries. Bioguided analysis and design of dairy processing side streams will result in the transformation of waste into isolated functional ingredients to add value to dietary products.