Protocol for constructing glycan biosynthetic networks using glycowork.

Glycans, present across all domains of life, comprise a wide range of monosaccharides assembled into complex, branching structures. Here, we present an in silico protocol to construct biosynthetic networks from a list of observed glycans using the Python package glycowork. We describe steps for data preparation, network construction, feature analysis, and data export. This protocol is implemented in Python using example data and can be adapted for use with customized datasets. For complete details on the use and execution of this protocol, please refer to Thomès et al.1.

Elucidating the glycan-binding specificity and structure of Cucumis melo agglutinin, a new R-type lectin.

Plant lectins have garnered attention for their roles as laboratory probes and potential therapeutics. Here, we report the discovery and characterization of Cucumis melo agglutinin (CMA1), a new R-type lectin from melon. Our findings reveal CMA1's unique glycan-binding profile, mechanistically explained by its 3D structure, augmenting our understanding of R-type lectins. We expressed CMA1 recombinantly and assessed its binding specificity using multiple glycan arrays, covering 1,046 unique sequences. This resulted in a complex binding profile, strongly preferring C2-substituted, beta-linked galactose (both GalNAc and Fuca1-2Gal), which we contrasted with the established R-type lectin Ricinus communis agglutinin 1 (RCA1). We also report binding of specific glycosaminoglycan subtypes and a general enhancement of binding by sulfation. Further validation using agglutination, thermal shift assays, and surface plasmon resonance confirmed and quantified this binding specificity in solution. Finally, we solved the high-resolution structure of the CMA1 N-terminal domain using X-ray crystallography, supporting our functional findings at the molecular level. Our study provides a comprehensive understanding of CMA1, laying the groundwork for further exploration of its biological and therapeutic potential.

The evolving world of milk oligosaccharides: Biochemical diversity understood by computational advances.

Milk oligosaccharides, complex carbohydrates unique to mammalian milk, play crucial roles in infant nutrition and immune development. This review explores their biochemical diversity, tracing the evolutionary paths that have led to their variation across different species. We highlight the intersection of nutrition, biology, and chemistry in understanding these compounds. Additionally, we discuss the latest computational methods and analytical techniques that have revolutionized the study of milk oligosaccharides, offering insights into their structural complexity and functional roles. This brief but essential review not only aims to provide a deeper understanding of milk oligosaccharides but also discuss the road toward their potential applications.

Decoding glycomics with a suite of methods for differential expression analysis.

Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.

Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc.

Breast milk is abundant with functionalized milk oligosaccharides (MOs) to nourish and protect the neonate. Yet we lack a comprehensive understanding of the repertoire and evolution of MOs across Mammalia. We report ∼400 MO-species associations (>100 novel structures) from milk glycomics of nine mostly understudied species: alpaca, beluga whale, black rhinoceros, bottlenose dolphin, impala, L'Hoest's monkey, pygmy hippopotamus, domestic sheep, and striped dolphin. This revealed the hitherto unknown existence of the LacdiNAc motif (GalNAcß1-4GlcNAc) in MOs of all species except alpaca, sheep, and striped dolphin, indicating the widespread occurrence of this potentially antimicrobial motif in MOs. We also characterize glucuronic acid-containing MOs in the milk of impala, dolphins, sheep, and rhinoceros, previously only reported in cows. We demonstrate that these GlcA-MOs exhibit potent immunomodulatory effects. Our study extends the number of known MOs by >15%. Combined with >1900 curated MO-species associations, we characterize MO motif distributions, presenting an exhaustive overview of MO biodiversity.

GlycoDraw: a python implementation for generating high-quality glycan figures.

Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFGs) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g. differential abundance data or glycomics mass spectra.

Mammalian milk glycomes: Connecting the dots between evolutionary conservation and biosynthetic pathways.

Milk oligosaccharides (MOs) are among the most abundant constituents of breast milk and are essential for health and development. Biosynthesized from monosaccharides into complex sequences, MOs differ considerably between taxonomic groups. Even human MO biosynthesis is insufficiently understood, hampering evolutionary and functional analyses. Using a comprehensive resource of all published MOs from >100 mammals, we develop a pipeline for generating and analyzing MO biosynthetic networks. We then use evolutionary relationships and inferred intermediates of these networks to discover (1) systematic glycome biases, (2) biosynthetic restrictions, such as reaction path preference, and (3) conserved biosynthetic modules. This allows us to prune and pinpoint biosynthetic pathways despite missing information. Machine learning and network analysis cluster species by their milk glycome, identifying characteristic sequence relationships and evolutionary gains/losses of motifs, MOs, and biosynthetic modules. These resources and analyses will advance our understanding of glycan biosynthesis and the evolution of breast milk.

Identification of New L-Fucosyl and L-Galactosyl Amides as Glycomimetic Ligands of TNF Lectin Domain of BC2L-C from Burkholderia cenocepacia.

The inhibition of carbohydrate-lectin interactions is being explored as an efficient approach to anti adhesion therapy and biofilm destabilization, two alternative antimicrobial strategies that are being explored against resistant pathogens. BC2L-C is a new type of lectin from Burkholderia cenocepacia that binds (mammalian) fucosides at the N-terminal domain and (bacterial) mannosides at the C-terminal domain. This double carbohydrate specificity allows the lectin to crosslink host cells and bacterial cells. We have recently reported the design and generation of the first glycomimetic antagonists of BC2L-C, ß-C- or ß-N-fucosides that target the fucose-specific N-terminal domain (BC2L-C-Nt). The low water solubility of the designed N-fucosides prevented a full examination of this promising series of ligands. In this work, we describe the synthesis and biophysical evaluation of new L-fucosyl and L-galactosyl amides, designed to be water soluble and to interact with BC2L-C-Nt. The protein-ligand interaction was investigated by Saturation Transfer Difference NMR, Isothermal Titration Calorimetry and crystallographic studies. STD-NMR experiments showed that both fucosyl and galactosyl amides compete with α-methyl fucoside for lectin binding. A new hit compound was identified with good water solubility and an affinity for BC2L-C-Nt of 159 µM (ITC), which represents a one order of magnitude gain over α-methyl fucoside. The x-ray structure of its complex with BC2L-C-Nt was solved at 1.55 Å resolution.

Structural insights into host-microbe glycointeractions.

Despite their ubiquitous presence in biological systems, glycans have historically received less attention than they deserved. Investigations in recent years have featured important findings about the role of glycans in regulating the human gut microbiota. Here, we present a brief overview of current trends that shape future directions of computational and experimental research approaches and add to our understanding of host-microbe glycointeractions.

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.

Transcriptome and Function of Novel Immunosuppressive Autoreactive Invariant Natural Killer T Cells That Are Absent in Progressive Multiple Sclerosis.

BACKGROUND AND OBJECTIVE: The aim of this study was to determine whether natural killer T (NKT) cells, including invariant (i) NKT cells, have clinical value in preventing the progression of multiple sclerosis (MS) by examining the mechanisms by which a distinct self-peptide induces a novel, protective invariant natural killer T cell (iNKT cell) subset. METHODS: We performed a transcriptomic and functional analysis of iNKT cells that were reactive to a human collagen type II self-peptide, hCII707-721, measuring differentially induced genes, cytokines, and suppressive capacity. RESULTS: We report the first transcriptomic profile of human conventional vs novel hCII707-721-reactive iNKT cells. We determined that hCII707-721 induces protective iNKT cells that are found in the blood of healthy individuals but not progressive patients with MS (PMS). By transcriptomic analysis, we observed that hCII707-721 promotes their development and proliferation, favoring the splicing of full-length AKT serine/threonine kinase 1 (AKT1) and effector function of this unique lineage by upregulating tumor necrosis factor (TNF)-related genes. Furthermore, hCII707-721-reactive iNKT cells did not upregulate interferon (IFN)-γ, interleukin (IL)-4, IL-10, IL-13, or IL-17 by RNA-seq or at the protein level, unlike the response to the glycolipid alpha-galactosylceramide. hCII707-721-reactive iNKT cells increased TNFα only at the protein level and suppressed autologous-activated T cells through FAS-FAS ligand (FAS-FASL) and TNFα-TNF receptor I signaling but not TNF receptor II. DISCUSSION: Based on their immunomodulatory properties, NKT cells have a potential value in the treatment of autoimmune diseases, such as MS. These significant findings suggest that endogenous peptide ligands can be used to expand iNKT cells, without causing a cytokine storm, constituting a potential immunotherapy for autoimmune conditions, including PMS.