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

Abstract

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 glycan microarray analysis of 116 commercially available lectins, including different preparations of the same lectin, to extract the specific glycan features required for lectin binding. Data was obtained using the Consortium for Functional Glycomics microarray (CFG v5.0) containing 611 glycans. We use a combination of machine learning algorithms to define lectin specificity, mapping inputs (glycan sequences) to outputs (lectin-glycan binding) for a large-scale evaluation of lectin-glycan binding behaviours. 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. Using a combination of machine learning and manual annotation of the data, we created 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.

Carbohydrate Sulfation As a Mechanism for Fine-Tuning Siglec Ligands

“…Likewise, a decrease in sLe X structures was observed in the CHST2- and Gal3ST4-overexpressing cell lysates. The enhanced binding of MAA upon 3- O -sulfation at Gal was expected based on the known specificity of MAA. , Although it is possible that overexpression of Gal3ST2 or Gal3ST3 could lead to downregulated expression or activity of one or more members of the ST3Gal family of sialyltransferases, through an undefined mechanism, a much more likely scenario is that Gal3ST2/3 compete for the same hydroxyl group as the ST3Gal family, as demonstrated previously . By flow cytometry, we found that SNA and MAA staining were consistent with the results from the lectin microarray (Figure S2a,b).…”

Applying transcriptomics to study glycosylation at the cell type level

“…Glycan recognition by receptors (e.g., antibodies ( Temme et al., 2021 ), selectins ( McEver, 2015 ), lectins ( Bojar et al., 2021 ), and carbohydrate-binding modules ( Lombard et al., 2014 )) mediates selective recognition of cells. The recognized features are generally well-defined with respect to terminal glycan structures as presented in Figure 4 D, whereas it appears that recognition for at least some glycan-binding proteins involve more complex features ( Cohen and Varki, 2014 ).…”

“…We cannot see the results of this process in a single cell using current structural analytics, and most of our understanding of the glycome stems from direct structural analysis of heterogeneous cell or tissue preparations, and thus informs us of an averaged snapshot of the glycan structures found in many cells ( Čaval et al., 2021 ; Khoo, 2019 ; Lageveen-Kammeijer et al., 2021 ; Levery et al., 2015 ; Thaysen-Andersen and Packer, 2014 ). An exception to this is the use of glycan-binding probes (lectins, antibodies, glycan-binding proteins) for binding to cells and tissues, and this approach, although limited by the number of specific probes to the majority of glycan structures ( Bojar et al., 2021 ), suggests that expression of select glycan structures can be highly regulated on cells during the cell cycle and during cellular maturation and differentiation ( Dabelsteen et al., 1991 ; Park et al., 2021 ; Thomas, 1971 ). Today, the best chances to circumvent the heterogeneity of cell populations come from technologies that have already broken the single cell barrier.…”

Glycan degradation promotes macroautophagy

“…Since FUCA1 is a glycosidase, we performed further histological analyses to determine if the vacuolation observed in multiple tissues was associated with accumulation of glycans. To test this, we utilized two fucose-binding lectins: Aleuria aurantia lectin (AAL), which recognizes fucose in multiple configurations ( 21 ), and Ulex europaeus agglutinin I (UEAI), which preferentially binds to fucose-linked α-1,2 to galactose ( 21 – 23 ). Using these lectins, we found that the accumulation of glycan species containing fucose in a number of different configurations was widespread in tissues lacking FUCA1 ( Fig.…”