Glycoblotting-Based Ovo-Sulphoglycomics Reveals Phosphorylated N-Glycans as a Possible Host Factor of AIV Prevalence in Waterfowls

Sulfated N-glycans play a crucial role in the interaction between influenza A virus (IAV) and its host. These glycans have been found to enhance viral replication, highlighting their significance in IAV propagation. This study investigated the expression of acidic N-glycans, specifically sulfated and phosphorylated glycans, in the egg whites of 72 avian species belonging to the Order Anseriformes (waterfowls). We used the glycoblotting-based sulphoglycomics approach to elucidate the diversity of acidic N-glycans and infer their potential role in protecting embryos from infections. Family-specific variations in sulfated and phosphorylated N-glycan profiles were identified in waterfowl egg whites. Different waterfowl species exhibited distinct expressions of sulfated trans-Gal(+) and trans-Gal(-) N-glycan structures. Additionally, species-specific expression of phosphorylated N-glycans was observed. Furthermore, it was found that waterfowl species with high avian influenza virus (AIV) prevalence displayed a higher abundance of phosphorylated hybrid and high-mannose N-glycans on their egg whites. These findings shed light on the importance of phosphorylated and sulfated N-glycans in understanding the role of acidic glycans in IAV propagation.

Site-selective sulfation of N-glycans by human GlcNAc-6-O-sulfotransferase 1 (CHST2) and chemoenzymatic synthesis of sulfated antibody glycoforms

Sulfation is a common modification of glycans and glycoproteins. Sulfated N-glycans have been identified in various glycoproteins and implicated for biological functions, but in vitro synthesis of structurally well-defined full length sulfated N-glycans remains to be described. We report here the first in vitro enzymatic sulfation of biantennary complex type N-glycans by recombinant human CHST2 (GlcNAc-6-O-sulfotransferase 1, GlcNAc6ST-1). We found that the sulfotransferase showed high antennary preference and could selectively sulfate the GlcNAc moiety located on the Manα1,3Man arm of the biantennary N-glycan. The glycan chain was further elongated by bacterial β1,4 galactosyltransferase from Neiserria meningitidis and human β1,4 galactosyltransferase IV（B4GALT4）, which led to the formation of different sulfated N-glycans. Using rituximab as a model IgG antibody, we further demonstrated that the sulfated N-glycans could be efficiently transferred to an intact antibody by using a chemoenzymatic Fc glycan remodeling method, providing homogeneous sulfated glycoforms of antibodies. Preliminary binding analysis indicated that sulfation did not affect the apparent affinity of the antibody for FcγIIIa receptor.

Sialylated and sulfated N-Glycans in MDCK and engineered MDCK cells for influenza virus studies

The Madin-Darby canine kidney (MDCK) cell line is an in vitro model for influenza A virus (IAV) infection and propagation. MDCK-SIAT1 (SIAT1) and humanized MDCK (hCK) cell lines are engineered MDCK cells that express N-glycans with elevated levels of sialic acid (Sia) in α2,6-linkage (α2,6-Sia) that are recognized by many human IAVs. To characterize the N-glycan structures in these cells and the potential changes compared to the parental MDCK cell line resulting from engineering, we analyzed the N-glycans from these cells at different passages, using both mass spectrometry and specific lectin and antibody binding. We observed significant differences between the three cell lines in overall complex N-glycans and terminal galactose modifications. MDCK cells express core fucosylated, bisected complex-type N-glycans at all passage stages, in addition to expressing α2,6-Sia on short N-glycans and α2,3-Sia on larger N-glycans. By contrast, SIAT1 cells predominantly express α2,6-Sia glycans and greatly reduced level of α2,3-Sia glycans. Additionally, they express bisected, sialylated N-glycans that are scant in MDCK cells. The hCK cells exclusively express α2,6-Sia glycans. Unexpectedly, hCK glycoproteins bound robustly to the plant lectin MAL-1, indicating α2,3-Sia glycans, but such binding was not Sia-dependent and closely mirrored that of an antibody that recognizes glycans with terminal 3-O-sulfate galactose (3-O-SGal). The 3-O-SGal epitope is highly expressed in N-glycans on multiple hCK glycoproteins. These results indicate vastly different N-glycomes between MDCK cells and the engineered clones that could relate to IAV infectivity.

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.

Sulfated glycans containing NeuAcα2-3Gal facilitate the propagation of human H1N1 influenza A viruses in eggs

When human influenza viruses are isolated and passaged in chicken embryos, variants with amino acid substitutions around the receptor binding site of hemagglutinin (HA) are selected; however, the mechanisms that underlie this phenomenon have yet to be elucidated. Here, we analyzed the receptor structures that contributed to propagation of egg-passaged human H1N1 viruses. The analysis included seasonal and 2009 pandemic strains, both of which have amino acid substitutions of HA found in strains isolated or passaged in eggs. These viruses exhibited high binding to sulfated glycans containing NeuAcα2-3Gal. In MDCK cells overexpressing the sulfotransferase that synthesize Galβ1-4(SO3--6)GlcNAc, production of human H1N1 viruses was increased up to 90-fold. Furthermore, these sulfated glycans were expressed on the allantoic and amniotic membranes of chicken embryos. These results suggest that 6-sulfo sialyl Lewis X and/or NeuAcα2-3Galβ1-4(SO3--6)GlcNAc are involved in efficient propagation of human H1N1 viruses in chicken embryos.

Using graph convolutional neural networks to learn a representation for glycans

As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes—from protein folding to viral cell entry—yet are still not well understood. There are few computational methods to link glycan sequences to functions, and they do not fully leverage all available information about glycans. SweetNet is a graph convolutional neural network that uses graph representation learning to facilitate a computational understanding of glycobiology. SweetNet explicitly incorporates the nonlinear nature of glycans and establishes a framework to map any glycan sequence to a representation. We show that SweetNet outperforms other computational methods in predicting glycan properties on all reported tasks. More importantly, we show that glycan representations, learned by SweetNet, are predictive of organismal phenotypic and environmental properties. Finally, we use glycan-focused machine learning to predict viral glycan binding, which can be used to discover viral receptors.

Burkholz et al. develop an analysis platform for glycans, using graph convolutional neural networks, that considers the branched nature of these carbohydrates. They demonstrate that glycan-focused machine learning can be employed for various purposes, such as to cluster species according to their glycomic similarity or to identify viral receptors.

Glycomics and glycoproteomics of viruses: Mass spectrometry applications and insights toward structure-function relationships

The advancement of viral glycomics has paralleled that of the mass spectrometry glycomics toolbox. In some regard the glycoproteins studied have provided the impetus for this advancement. Viral proteins are often highly glycosylated, especially those targeted by the host immune system. Glycosylation tends to be dynamic over time as viruses propagate in host populations leading to increased number of and/or "movement" of glycosylation sites in response to the immune system and other pressures. This relationship can lead to highly glycosylated, difficult to analyze glycoproteins that challenge the capabilities of modern mass spectrometry. In this review, we briefly discuss five general areas where glycosylation is important in the viral niche and how mass spectrometry has been used to reveal key information regarding structure-function relationships between viral glycoproteins and host cells. We describe the recent past and current glycomics toolbox used in these analyses and give examples of how the requirement to analyze these complex glycoproteins has provided the incentive for some advances seen in glycomics mass spectrometry. A general overview of viral glycomics, special cases, mass spectrometry methods and work-flows, informatics and complementary chemical techniques currently used are discussed. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

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.

Application of network smoothing to glycan LC-MS profiling

Motivation

Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub-structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity.

Results

We developed an algorithm for assigning glycan compositions from LC-MS data by exploring biosynthetic network relationships among glycans. Our algorithm optimizes a set of likelihood scoring functions based on glycan chemical properties but uses network Laplacian regularization and optionally prior information about expected glycan families to smooth the likelihood and thus achieve a consistent and more representative solution. Our method was able to identify as many, or more glycan compositions compared to previous approaches, and demonstrated greater sensitivity with regularization. Our network definition was tailored to N-glycans but the method may be applied to glycomics data from other glycan families like O-glycans or heparan sulfate where the relationships between compositions can be expressed as a graph.

Availability and implementation Built Executable

http://www.bumc.bu.edu/msr/glycresoft/ and Source Code: https://github.com/BostonUniversityCBMS/glycresoft.

Supplementary information

Supplementary data are available at Bioinformatics online.

Topological N-glycosylation and site-specific N-glycan sulfation of influenza proteins in the highly expressed H1N1 candidate vaccines

The outbreak of a pandemic influenza H1N1 in 2009 required the rapid generation of high-yielding vaccines against the A/California/7/2009 virus, which were achieved by either addition or deletion of a glycosylation site in the influenza proteins hemagglutinin and neuraminidase. In this report, we have systematically evaluated the glycan composition, structural distribution and topology of glycosylation for two high-yield candidate reassortant vaccines (NIBRG-121xp and NYMC-X181A) by combining various enzymatic digestions with high performance liquid chromatography and multiple-stage mass spectrometry. Proteomic data analyses of the full-length protein sequences determined 9 N-glycosylation sites of hemagglutinin, and defined 6 N-glycosylation sites and the glycan structures of low abundance neuraminidase, which were occupied by high-mannose, hybrid and complex-type N-glycans. A total of ~300 glycopeptides were analyzed and manually validated by tandem mass spectrometry. The specific N-glycan structure and topological location of these N-glycans are highly correlated to the spatial protein structure and the residential ligand binding. Interestingly, sulfation, fucosylation and bisecting N-acetylglucosamine of N-glycans were also reliably identified at the specific glycosylation sites of the two influenza proteins that may serve a crucial role in regulating the protein structure and increasing the protein abundance of the influenza virus reassortants.

The minimum information required for a glycomics experiment (MIRAGE) project: improving the standards for reporting glycan microarray-based data

MIRAGE (Minimum Information Required for AGlycomics Experiment) is an initiative that was created by experts in the fields of glycobiology, glycoanalytics and glycoinformatics to produce guidelines for reporting results from the diverse types of experiments and analyses used in structural and functional studies of glycans in the scientific literature. As a sequel to the guidelines for sample preparation (Struwe et al. 2016, Glycobiology, 26:907–910) and mass spectrometry  data (Kolarich et al. 2013, Mol. Cell Proteomics, 12:991–995), here we present the first version of guidelines intended to improve the standards for reporting data from glycan microarray analyses. For each of eight areas in the workflow of a glycan microarray experiment, we provide guidelines for the minimal information that should be provided in reporting results. We hope that the MIRAGE glycan microarray guidelines proposed here will gain broad acceptance by the community, and will facilitate interpretation and reproducibility of the glycan microarray results with implications in comparison of data from different laboratories and eventual deposition of glycan microarray data in international databases.

Association analyses of large-scale glycan microarray data reveal novel host-specific substructures in influenza A virus binding glycans

Influenza A viruses can infect a wide variety of animal species and, occasionally, humans. Infection occurs through the binding formed by viral surface glycoprotein hemagglutinin and certain types of glycan receptors on host cell membranes. Studies have shown that the α2,3-linked sialic acid motif (SA2,3Gal) in avian, equine, and canine species; the α2,6-linked sialic acid motif (SA2,6Gal) in humans; and SA2,3Gal and SA2,6Gal in swine are responsible for the corresponding host tropisms. However, more detailed and refined substructures that determine host tropisms are still not clear. Thus, in this study, we applied association mining on a set of glycan microarray data for 211 influenza viruses from five host groups: humans, swine, canine, migratory waterfowl, and terrestrial birds. The results suggest that besides Neu5Acα2-6Galβ, human-origin viruses could bind glycans with Neu5Acα2-8Neu5Acα2-8Neu5Ac and Neu5Gcα2-6Galβ1-4GlcNAc substructures; Galβ and GlcNAcβ terminal substructures, without sialic acid branches, were associated with the binding of human-, swine-, and avian-origin viruses; sulfated Neu5Acα2-3 substructures were associated with the binding of human- and swine-origin viruses. Finally, through three-dimensional structure characterization, we revealed that the role of glycan chain shapes is more important than that of torsion angles or of overall structural similarities in virus host tropisms.