The Role of Fucose-Containing Glycan Motifs Across Taxonomic Kingdoms

Abstract

The extraordinary diversity of glycans leads to large differences in the glycomes of different kingdoms of life. Yet, while most monosaccharides are solely found in certain taxonomic groups, there is a small set of monosaccharides with widespread distribution across nearly all domains of life. These general monosaccharides are particularly relevant for glycan motifs, as they can readily be used by commensals and pathogens to mimic host glycans or hijack existing glycan recognition systems. Among these, the monosaccharide fucose is especially interesting, as it frequently presents itself as a terminal monosaccharide, primed for interaction with proteins. Here, we analyze fucose-containing glycan motifs across all taxonomic kingdoms. Using a hereby presented large species-specific glycan dataset and a plethora of methods for glycan-focused bioinformatics and machine learning, we identify characteristic as well as shared fucose-containing glycan motifs for various taxonomic groups, demonstrating clear differences in fucose usage. Even within domains, fucose is used differentially based on an organism’s physiology and habitat. We particularly highlight differences in fucose-containing motifs between vertebrates and invertebrates. With the example of pathogenic and non-pathogenic Escherichia coli strains, we also demonstrate the importance of fucose-containing motifs in molecular mimicry and thereby pathogenic potential. We envision that this study will shed light on an important class of glycan motifs, with potential new insights into the role of fucosylated glycans in symbiosis, pathogenicity, and immunity.

Comparative Mucomic Analysis of Three Functionally DistinctCornu aspersumSecretions

“…This observation supports previous accounts of low levels of fucosylation in invertebrate O -glycans. 64 The presence of methylation in both the N- and O -glycans supports prior reports on both marine and land snails, and identified compositions are similar to those identified in other snails. 65 A difference is the degree of methylation, as previous studies on other species reported high levels (3 – 4 methyl groups per monosaccharide), while our studies indicate lower levels of methylation (0 – 2 methyl groups).…”

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“…In glycobiology, deep learning has recently enabled new analyses of sequence–function relationships. [ 17 , 18 ] Based on this, we developed SweetNet, [ 8 ] a graph convolutional neural network method that learns glycan representations by taking their branching structures into account. Briefly, SweetNet considers glycans as molecular graphs, with monosaccharides and linkages as nodes.…”

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“…Next to being used for downstream models that can use these similarities as new features for prediction, learned glycan similarities can be used to visualize clusters of related glycans, for instance via t-SNE or UMAP mentioned above, and allow for interpretation of learned glycan associations. In a recent study, this has been for instance used for an in-depth investigation of the role and properties of different fucose-containing motifs across taxonomic kingdoms …”

“…In a recent study, this has been for instance used for an in-depth investigation of the role and properties of different fucose-containing motifs across taxonomic kingdoms. 127…”